Search ICLR 2019

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"sentence representations" has 45 results

The Natural Language Decathlon: Multitask Learning as Question Answering    

tl;dr We introduce a multitask learning challenge that spans ten natural language processing tasks and propose a new model that jointly learns them.

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new multitask question answering network (MQAN) that jointly learns all tasks in decaNLP without any task-specific modules or parameters more effectively than sequence-to-sequence and reading comprehension baselines. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and that performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.

Fake Sentence Detection as a Training Task for Sentence Encoding    

No tl;dr =[

Sentence encoders are typically trained on generative language modeling tasks with large unlabeled datasets. While these encoders achieve strong results on many sentence-level tasks, they are difficult to train with long training cycles. We introduce fake sentence detection as a new discriminative training task for learning sentence encoders. We automatically generate fake sentences by corrupting original sentences from a source collection and train the encoders to produce representations that are effective at detecting fake sentences. This binary classification task turns to be quite efficient for training sentence encoders. We compare a basic BiLSTM encoder trained on this task with strong sentence encoding models (Skipthought and FastSent) trained on a language modeling task. We find that the BiLSTM trains much faster on fake sentence detection (20 hours instead of weeks) using smaller amounts of data (1M instead of 64M sentences). Further analysis shows the learned representations also capture many syntactic and semantic properties expected from good sentence representations.

Learning Grounded Sentence Representations by Jointly Using Video and Text Information    

tl;dr We propose a joint model to incorporate visual knowledge in sentence representations

Visual grounding of language is an active research field aiming at enriching text-based representations with visual information. In this paper, we propose a new way to leverage visual knowledge for sentence representations. Our approach transfers the structure of a visual representation space to the textual space by using two complementary sources of information: (1) the cluster information: the implicit knowledge that two sentences associated with the same visual content describe the same underlying reality and (2) the perceptual information contained within the structure of the visual space. We use a joint approach to encourage beneficial interactions during training between textual, perceptual, and cluster information. We demonstrate the quality of the learned representations on semantic relatedness, classification, and cross-modal retrieval tasks.

Bilingual-GAN: Neural Text Generation and Neural Machine Translation as Two Sides of the Same Coin    

tl;dr We present a novel method for Bilingual Text Generation producing parallel concurrent sentences in two languages.

Latent space based GAN methods and attention based encoder-decoder architectures have achieved impressive results in text generation and Unsupervised NMT respectively. Leveraging the two domains, we propose an adversarial latent space based architecture capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is adversarially constrained to be shared between both languages. First an NMT model is trained, with back-translation and an adversarial setup, to enforce a latent state between the two languages. The encoder and decoder are shared for the two translation directions. Next, a GAN is trained to generate ‘synthetic’ code mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both Supervised and Unsupervised NMT.

Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model    

tl;dr State-of-the-art zero-shot learning performance by using a translation task to bridge multi-task training across languages.

Neural language models have been shown to achieve an impressive level of performance on a number of language processing tasks. The majority of these models, however, are limited to producing predictions for only English texts due to limited amounts of labeled data available in other languages. One potential method for overcoming this issue is learning cross-lingual text representations that can be used to transfer the performance from training on English tasks to non-English tasks, despite little to no task-specific non-English data. In this paper, we explore a natural setup for learning cross-lingual sentence representations: the dual-encoder. We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.

RNNs implicitly implement tensor-product representations    

tl;dr RNNs implicitly implement tensor-product representations, a principled and interpretable method for representing symbolic structures in continuous space.

Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors for roles (e.g., sequence position) and vectors for fillers (e.g., a particular word). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations; those representation exhibit highly interpretable compositional structure. We explore the settings that lead RNNs to induce such structure-sensitive representations. By contrast with these results, TPDN experiments with four standard sentence encoding models showed that those sentence encodings could be largely approximated using bag-of-words representations, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated by TPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural properties.

Countering Language Drift via Grounding    

tl;dr Grounding helps avoid language drift during fine-tuning natural language agents with policy gradients.

While reinforcement learning (RL) shows a lot of promise for natural language processing—e.g. when fine-tuning natural language systems for optimizing a certain objective—there has been little investigation into potential language drift: when an external reward is used to train a system, the agents’ communication protocol may easily and radically diverge from natural language. By re-casting translation as a communication game, we show that language drift indeed happens when pre-trained agents are fine-tuned with policy gradient methods. We contend that simply adding a "naturalness" constraint to the reward, e.g. by using language model log likelihood, does not fully address the issue, and argue that (perceptual) grounding is required. That is, while language model constraints impose syntactic conformity, they do not lead to semantic correspondence. Our experiments show that grounded models give the best communication performance, while retaining English syntax along with the ability to convey the intended semantics.

No Training Required: Exploring Random Encoders for Sentence Classification    

No tl;dr =[

We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking at how much modern sentence embeddings gain over random methods---as it turns out, surprisingly little; and by 2) providing the field with more appropriate baselines going forward---which are, as it turns out, quite strong. We also make important observations about proper experimental protocol for sentence classification evaluation, together with recommendations for future research.

Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling    

tl;dr We compare many tasks and task combinations for pretraining sentence-level BiLSTMs for NLP tasks. Language modeling is the best single pretraining task, but simple baselines also do well.

Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018). This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work.

Unsupervised Learning of Sentence Representations Using Sequence Consistency    

tl;dr Good sentence encoders can be learned by training them to distinguish between consistent and inconsistent (pairs of) sequences that are generated in an unsupervised manner.

Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a simple, yet surprisingly powerful unsupervised method to learn such representations by enforcing consistency constraints on sequences of tokens. We consider two classes of such constraints -- sequences that form a sentence and between two sequences that form a sentence when merged. We learn a sentence encoder by training it to distinguish between consistent and inconsistent examples. Extensive evaluation on several transfer learning and linguistic probing tasks shows improved performance over strong unsupervised and supervised baselines, substantially surpassing them in several cases.

An Information-Theoretic Metric of Transferability for Task Transfer Learning    

tl;dr We present a provable and easily-computable evaluation function that estimates the performance of transferred representations from one learning task to another in task transfer learning.

An important question in task transfer learning is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems. Inspired by a principled information theoretic approach, H-score has a direct connection to the asymptotic error probability of the decision function based on the transferred feature. This formulation of transferability can further be used to select a suitable set of source tasks in task transfer learning problems or to devise efficient transfer learning policies. Experiments using both synthetic and real image data show that not only our formulation of transferability is meaningful in practice, but also it can generalize to inference problems beyond classification, such as recognition tasks for 3D indoor-scene understanding.

NLProlog: Reasoning with Weak Unification for Natural Language Question Answering    

tl;dr We introduce NLProlog, a system that performs rule-based reasoning on natural language by leveraging pretrained sentence embeddings and fine-tuning with Evolution Strategies, and apply it to two multi-hop Question Answering tasks.

Symbolic logic allows practitioners to build systems that perform rule-based reasoning which is interpretable and which can easily be augmented with prior knowledge. However, such systems are traditionally difficult to apply to problems involving natural language due to the large linguistic variability of language. Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability. We propose to reap the benefits of both approaches by applying a combination of neural networks and logic programming to natural language question answering. We propose to employ an external, non-differentiable Prolog prover which utilizes a similarity function over pretrained sentence encoders. We fine-tune these representations via Evolution Strategies with the goal of multi-hop reasoning on natural language. This allows us to create a system that can apply rule-based reasoning to natural language and induce domain-specific natural language rules from training data. We evaluate the proposed system on two different question answering tasks, showing that it complements two very strong baselines – BIDAF (Seo et al., 2016a) and FASTQA (Weissenborn et al.,2017) – and outperforms both when used in an ensemble.

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model    

tl;dr We present a novel training scheme for efficiently obtaining order-aware sentence representations.

Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model improves the performance over CBOW for 8 out of 11 supervised downstream tasks with an average improvement of 1.2%.

Multiple-Attribute Text Rewriting    

tl;dr A system for rewriting text conditioned on multiple controllable attributes

The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style". In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training, that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.

Classification from Positive, Unlabeled and Biased Negative Data    

tl;dr This paper studied the PUbN classification problem, where we incorporate biased negative (bN) data, i.e., negative data that is not fully representative of the true underlying negative distribution, into positive-unlabeled (PU) learning.

Positive-unlabeled (PU) learning addresses the problem of learning a binary classifier from positive (P) and unlabeled (U) data. It is often applied to situations where negative (N) data are difficult to be fully labeled. However, collecting a non-representative N set that contains only a small portion of all possible N data can be much easier in many practical situations. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. The fact that the training N data are biased also makes our work very different from those of standard semi-supervised learning. We provide an empirical risk minimization-based method to address this PUbN classification problem. Our approach can be regarded as a variant of traditional example-reweighting algorithms, with the weight of each example computed through a preliminary step that draws inspiration from PU learning. We also derive an estimation error bound for the proposed method. Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU leaning scenarios on several benchmark datasets.

Is Wasserstein all you need?    

No tl;dr =[

We propose a unified framework for building unsupervised representations of entities and their compositions, by viewing each entity as a histogram over its contexts. This enables us to take advantage of optimal transport and construct representations that effectively harness the geometry of the underlying space containing the contexts. Our method captures uncertainty via modelling the entities as distributions and simultaneously provides interpretability with the optimal transport map, hence giving a novel perspective for building rich and powerful feature representations. As a guiding example, we formulate unsupervised representations for text, and demonstrate it on tasks such as sentence similarity and word entailment detection. Empirical results show strong advantages gained through the proposed framework. This approach can be used for any unsupervised or supervised problem (on text or other modalities) with a co-occurrence structure, such as any sequence data. The key tools at the core of this framework are Wasserstein distances and Wasserstein barycenters, hence raising the question from our title.

Engaging Image Captioning Via Personality    

tl;dr We develop engaging image captioning models conditioned on personality that are also state of the art on regular captioning tasks.

Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, Personality-Captions, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits.We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations (Mazaré et al., 2018) with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.

Hierarchically-Structured Variational Autoencoders for Long Text Generation    

tl;dr Propose a hierarchically-structured variational autoencoder for generating long and coherent units of text

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation. Existing methods primarily focus on synthesizing relatively short sentences (with less than twenty words). In this paper, we propose a novel framework, hierarchically-structured variational autoencoder (hier-VAE), for generating long and coherent units of text. To enhance the model’s plan-ahead ability, intermediate sentence representations are introduced into the generative networks to guide the word-level predictions. To alleviate the typical optimization challenges associated with textual VAEs, we further employ a hierarchy of stochastic layers between the encoder and decoder networks. Extensive experiments are conducted to evaluate the proposed method, where hier-VAE is shown to make effective use of the latent codes and achieve lower perplexity relative to language models. Moreover, the generated samples from hier-VAE also exhibit superior quality according to both automatic and human evaluations.

Multi-Grained Entity Proposal Network for Named Entity Recognition    

No tl;dr =[

In this paper, we focus on a new Named Entity Recognition (NER) task, i.e., the Multi-grained NER task. This task aims to simultaneously detect both fine-grained and coarse-grained entities in sentences. Correspondingly, we develop a novel Multi-grained Entity Proposal Network (MGEPN). Different from traditional NER models which regard NER as a sequential labeling task, MGEPN provides a new method that proposes entity candidates in the Proposal Network and classifies entities into different categories in the Classification Network. All possible entity candidates including fine-grained ones and coarse-grained ones are proposed in the Proposal Network, which enables the MGEPN model to identify multi-grained entities. In order to better identify named entities and determine their categories, context information is utilized and transferred from the Proposal Network to the Classification Network during the learning process. A novel Entity-Context attention mechanism is also introduced to help the model focus on entity-related context information. Experiments show that our model can obtain state-of-the-art performance on two real-world datasets for both the Multi-grained NER task and the traditional NER task.

ACTRCE: Augmenting Experience via Teacher’s Advice    

tl;dr Combine language goal representation with hindsight experience replays.

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failure experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, but we also found that little amount of hindsight advice is sufficient for the learning to take off, showing the practical aspect of the method.

Unsupervised Document Representation using Partition Word-Vectors Averaging    

tl;dr A simple unsupervised method for multi-sentense-document embedding using partition based word vectors averaging that achieve results comparable to sophisticated models.

Learning effective document-level representation is essential in many important NLP tasks such as document classification, summarization, etc. Recent research has shown that simple weighted averaging of word vectors is an effective way to represent sentences, often outperforming complicated seq2seq neural models in many tasks. While it is desirable to use the same method to represent documents as well, unfortunately, the effectiveness is lost when representing long documents involving multiple sentences. One reason for this degradation is due to the fact that a longer document is likely to contain words from many different themes (or topics), and hence creating a single vector while ignoring all the thematic structure is unlikely to yield an effective representation of the document. This problem is less acute in single sentences and other short text fragments where presence of a single theme/topic is most likely. To overcome this problem, in this paper we present PSIF, a partitioned word averaging model to represent long documents. P-SIF retains the simplicity of simple weighted word averaging while taking a document's thematic structure into account. In particular, P-SIF learns topic-specific vectors from a document and finally concatenates them all to represent the overall document. Through our experiments over multiple real-world datasets and tasks, we demonstrate PSIF's effectiveness compared to simple weighted averaging and many other state-of-the-art baselines. We also show that PSIF is particularly effective in representing long multi-sentence documents. We will release PSIF's embedding source code and data-sets for reproducing results.

Measuring Density and Similarity of Task Relevant Information in Neural Representations    

tl;dr Measuring information density and cross-task similarity in neural models and its application in transfer learning.

Neural models achieve state-of-the-art performance due to their ability to extract salient features useful to downstream tasks. However, our understanding of how this task-relevant information is included in these networks is still incomplete. In this paper, we examine two questions (1) how densely is information included in extracted representations, and (2) how similar is the encoding of relevant information between related tasks. We propose metrics to measure information density and cross-task similarity, and perform an extensive analysis in the domain of natural language processing, using four varieties of sentence representation and 13 tasks. We also demonstrate how the proposed analysis tools can find immediate use in choosing tasks for transfer learning.

Text Embeddings for Retrieval from a Large Knowledge Base    

tl;dr The new attempt for creating semantically meaningful text embeddings via improved language modeling and utilizing an extra knowledge base

Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way, we suggest the use of the Stanford Question Answering Dataset (SQuAD) in an open-domain question answering context, where the first task is to find paragraphs useful for answering a given question. First, we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with, and without IDF weighting. Our main results are that by training deep residual neural models specifically for retrieval purposes can yield significant gains when it is used to augment existing embeddings. We also establish that deeper models are superior to this task. The best base baseline embeddings augmented by our learned neural approach improves the top-1 recall of the system by 14% in terms of the question side, and by 8% in terms of the paragraph side.

Talk The Walk: Navigating Grids in New York City through Grounded Dialogue    

tl;dr First large-scale dialogue dataset grounded in action and perception

We introduce `"Talk The Walk", the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a 'guide' and a 'tourist') that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are described in detail, are challenging and their full solution is an open problem that we pose to the community. We (i) focus on the task of tourist localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding tourist utterances into the guide's map, (ii) show it yields significant improvements for both emergent and natural language communication, and (iii) using this method, we establish non-trivial baselines on the full task.

Improving Sentence Representations with Multi-view Frameworks    

tl;dr Multi-view learning improves unsupervised sentence representation learning

Multi-view learning can provide self-supervision when different views are available of the same data. Distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora. Motivated by the asymmetry in the two hemispheres of the human brain as well as the observation that different learning architectures tend to emphasise different aspects of sentence meaning, we present two multi-view frameworks for learning sentence representations in an unsupervised fashion. One framework uses a generative objective and the other a discriminative one. In both frameworks, the final representation is an ensemble of two views, in which, one view encodes the input sentence with a Recurrent Neural Network (RNN), and the other view encodes it with a simple linear model. We show that, after learning, the vectors produced by our multi-view frameworks provide improved representations over their single-view learned counterparts, and the combination of different views gives representational improvement over each view and demonstrates solid transferability on standard downstream tasks.


No tl;dr =[

This paper revisits the Random Walk model for sentence embedding in the context of non-extensive statistics. We propose a non-extensive algebra to compute the discourse vector. We argue that by doing so we are taking into account high non-linearity in the semantic space. Furthermore, we show that by considering a non-extensive algebra, the compounding effect of the vector length is mitigated. Overall, we show that the proposed model leads to good sentence embedding. We evaluate the embedding method on textual similarity tasks.

Learning to Decompose Compound Questions with Reinforcement Learning    

tl;dr We propose a learning-to-decompose agent that helps a simple-question answerer to answer compound question over knowledge graph.

As for knowledge-based question answering, a fundamental problem is to relax the assumption of answerable questions from simple questions to compound questions. Traditional approaches firstly detect topic entity mentioned in questions, then traverse the knowledge graph to find relations as a multi-hop path to answers, while we propose a novel approach to leverage simple-question answerer to answer compound questions. Our model consists of two components: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) a simple-question answerer that classifies the corresponding relation to answers. Experiments demonstrate that our model learns complex rules of compositionality as policy, which benefits a simple neural network to achieve state-of-the-art results on the most challenging research dataset. We analyze the interpretable decomposition process as well as generated partitions.

What do you learn from context? Probing for sentence structure in contextualized word representations    

tl;dr We probe for sentence structure in ELMo and related contextual embedding models. We find existing models efficiently encode syntax and show evidence of long-range dependencies, but only offer small improvements on semantic tasks.

Contextualized representation models such as CoVe (McCann et al., 2017) and ELMo (Peters et al., 2018a) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from three recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that ELMo encodes linguistic structure at the word level better than other comparable models, and that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer small improvements on semantic tasks over a noncontextual baseline.

Adversarial Decomposition of Text Representation    

tl;dr A method which learns separate representations for the meaning and the form of a sentence

In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, where each vector is responsible for a specific aspect of the input sentence. We evaluate the proposed method on two case studies: the conversion between different social registers and diachronic language change. We show that the proposed method is capable of fine-grained con- trolled change of these aspects of the input sentence. For example, our model is capable of learning a continuous (rather than categorical) representation of the style of the sentence, in line with the reality of language use. The model uses adversarial-motivational training and includes a special motivational loss, which acts opposite to the discriminator and encourages a better decomposition. Finally, we evaluate the obtained meaning embeddings on a downstream task of para- phrase detection and show that they are significantly better than embeddings of a regular autoencoder.

Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings    

No tl;dr =[

Word embeddings are known to boost performance of many NLP tasks such as text classification, meanwhile they can be enhanced by labels at the document level to capture nuanced meaning such as sentiment and topic. Can one combine these two research directions to benefit from both? In this paper, we propose to jointly train a text classifier with a label-enhanced and domain-aware word embedding model, using an unlabeled corpus and only a few labeled data from non-target domains. The embeddings are trained on the unlabed corpus and enhanced by pseudo labels coming from the classifier, and at the same time are used by the classifier as input and training signals. We formalize this symbiotic cycle in a variational Bayes framework, and show that our method improves both the embeddings and the text classifier, outperforming state-of-the-art domain adaptation and semi-supervised learning techniques. We conduct detailed ablative tests to reveal gains from important components of our approach. The source code and experiment data will be publicly released.

Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors    

tl;dr Max-pooled word vectors with fuzzy Jaccard set similarity are an extremely competitive baseline for semantic similarity; we propose a simple dynamic variant that performs even better.

Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchmarks. Inspired by these revelations, we push the limits of word embeddings even further. We propose a novel fuzzy bag-of-word (FBoW) representation for text that contains all the words in the vocabulary simultaneously but with different degrees of membership, which are derived from similarities between word vectors. We show that max-pooled word vectors are only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index rather than cosine similarity. Finally, we propose DynaMax, a completely unsupervised and non-parametric similarity measure that dynamically extracts and max-pools good features depending on the sentence pair. This method is both efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin when word vectors are trained unsupervised. When the word vectors are trained supervised to directly optimise cosine similarity, our measure is still comparable in performance despite being unrelated to the original objective.

Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning    

tl;dr We apply ideas from Statistical Relational Learning to compose sentence embeddings with more expressivity

Various NLP problems -- such as the prediction of sentence similarity, entailment, and discourse relations -- are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. We call them textual relational problems. A popular model for textual relational problems is to embed sentences into fixed size vectors and use composition functions (e.g. difference or concatenation) of those vectors as features for the prediction. Meanwhile, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this work, we prove that textual relational models implicitly use compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to solve textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.

TopicGAN: Unsupervised Text Generation from Explainable Latent Topics    

No tl;dr =[

Learning discrete representations of data and then generating data from the discovered representations have been increasingly studied, because the obtained discrete representations can benefit unsupervised learning. However, the performance of learning discrete representations of textual data with deep generative models has not been widely explored. In this work, we propose TopicGAN, a two-step generative model on text generation, which is able to discover discrete latent topics of texts and generate natural language from the discovered latent topics in an unsupervised fashion. Promising results are shown on unsupervised text classification and text generation for both subjective and objective evaluation.

Deep Frank-Wolfe For Neural Network Optimization    

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Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants. However, SGD requires a hand-designed schedule for the learning rate. In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule. We present an optimization method that offers the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter. Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design. Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step. We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD. We customize the algorithm in two ways to further improve its performance. First, we use a descent direction that smoothes the loss function to better condition the problem. Second, we combine our proximal algorithm with Nesterov momentum to benefit from acceleration. We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad. Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while converging faster.

Learning Robust, Transferable Sentence Representations for Text Classification    

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Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.

Learning protein sequence embeddings using information from structure    

tl;dr We present a method for learning protein sequence embedding models using structural information in the form of global structural similarity between proteins and within protein residue-residue contacts.

Inferring structural properties of a protein given only its amino acid sequence is a challenging problem. Existing approaches based solely on sequence are unable to recognize and exploit structural patterns when sequences have diverged too far. We introduce a novel framework for infusing structural information into position-specific representations of protein sequences. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism to incorporate (i) pairwise residue contact maps for individual proteins and (ii) co-membership in structural categories based on a curated database (SCOPe). For co-membership, we introduce soft symmetric alignment (SSA) between sequences of vector embeddings. We show empirically that our approach outperforms existing direct sequence alignment methods and also a structure-based alignment method when predicting structural similarity. SSA also enables learning informative position-specific embeddings even when no residue level supervision is available. Finally, we demonstrate that the learned embeddings can be transferred to other protein sequence problems, improving state-of-the-art in transmembrane domain prediction.

Towards Language Agnostic Universal Representations    

tl;dr By formalizing universal grammar as an optimization problem we learn language agnostic universal representations which we can utilize to do zero-shot learning across languages.

When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion. We learn these representations by taking inspiration from linguistics and formalizing Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970). We demonstrate the capabilities of these representations by showing that the models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding    

tl;dr We present a multi-task benchmark and analysis platform for evaluating generalization in natural language understanding systems.

For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.

On Accurate Evaluation of GANs for Language Generation    

tl;dr We discuss how to evaluate GANs for language generation, propose a protocol and show that simple Language Models achieve results as good as GANs.

Generative Adversarial Networks (GANs) are a promising approach to language generation. The latest works introducing novel GAN models for language generation use n-gram based metrics for evaluation and only report single scores of the best run. In this paper, we argue that this often misrepresents the true picture and does not tell the full story, as GAN models can be extremely sensitive to the random initialization and small deviations from the best hyperparameter choice. In particular, we demonstrate that the previously used BLEU score is not sensitive to semantic deterioration of generated texts and propose alternative metrics that better capture the quality and diversity of the generated samples. We also conduct a set of experiments comparing a number of GAN models for text with a conventional Language Model (LM) and find that none of the considered models performs convincingly better than the LM.

Zero-training Sentence Embedding via Orthogonal Basis    

tl;dr A simple and training-free approach for sentence embeddings with competitive performance compared with sophisticated models requiring either large amount of training data or prolonged training time.

We propose a simple and robust training-free approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is its novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representation. This approach requires zero training and zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Experimental results show that our model outperforms all existing zero-training alternatives in all the tasks and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.

Sentence Encoding with Tree-Constrained Relation Networks    

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The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.

Mitigating Bias in Natural Language Inference Using Adversarial Learning    

tl;dr Adversarial learning methods encourage NLI models to ignore dataset-specific biases and help models transfer across datasets.

Recognizing the relationship between two texts is an important aspect of natural language understanding (NLU), and a variety of neural network models have been proposed for solving NLU tasks. Unfortunately, recent work showed that the datasets these models are trained on often contain biases that allow models to achieve non-trivial performance without possibly learning the relationship between the two texts. We propose a framework for building robust models by using adversarial learning to encourage models to learn latent, bias-free representations. We test our approach in a Natural Language Inference (NLI) scenario, and show that our adversarially-trained models learn robust representations that ignore known dataset-specific biases. Our experiments demonstrate that our models are more robust to new NLI datasets.

Understanding and Improving Sequence-Labeling NER with Self-Attentive LSTMs    

tl;dr We provide insightful understanding of sequence-labeling NER and propose to use two types of cross structures, both of which bring theoretical and empirical improvements.

This paper improves upon the line of research that formulates named entity recognition (NER) as a sequence-labeling problem. We use so-called black-box long short-term memory (LSTM) encoders to achieve state-of-the-art results while providing insightful understanding of what the auto-regressive model learns with a parallel self-attention mechanism. Specifically, we decouple the sequence-labeling problem of NER into entity chunking, e.g., Barack_B Obama_E was_O elected_O, and entity typing, e.g., Barack_PERSON Obama_PERSON was_NONE elected_NONE, and analyze how the model learns to, or has difficulties in, capturing text patterns for each of the subtasks. The insights we gain then lead us to explore a more sophisticated deep cross-Bi-LSTM encoder, which proves better at capturing global interactions given both empirical results and a theoretical justification.

Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis    

tl;dr We throughly compare several pretraining tasks on their ability to induce syntactic information and find that representations from language models consistently perform best, even when trained on relatively small amounts of data.

Recent work using auxiliary prediction task classifiers to investigate the properties of LSTM representations has begun to shed light on why pretrained representations, like ELMo (Peters et al., 2018) and CoVe (McCann et al., 2017), are so beneficial for neural language understanding models. We still, though, do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn. With this in mind, we compare four objectives - language modeling, translation, skip-thought, and autoencoding - on their ability to induce syntactic and part-of-speech information. We make a fair comparison between the tasks by holding constant the quantity and genre of the training data, as well as the LSTM architecture. We find that representations from language models consistently perform best on our syntactic auxiliary prediction tasks, even when trained on relatively small amounts of data. These results suggest that language modeling may be the best data-rich pretraining task for transfer learning applications requiring syntactic information. We also find that the representations from randomly-initialized, frozen LSTMs perform strikingly well on our syntactic auxiliary tasks, but this effect disappears when the amount of training data for the auxiliary tasks is reduced.

Language Model Pre-training for Hierarchical Document Representations    

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Hierarchical neural architectures can efficiently capture long-distance dependencies and have been used for many document-level tasks such as summarization, document segmentation, and fine-grained sentiment analysis. However, effective usage of such a large context can difficult to learn, especially in the case where there is limited labeled data available. Building on the recent success of language model pretraining methods for learning flat representations of text, we propose algorithms for pre-training hierarchical document representations from unlabeled data. Unlike prior work, which has focused on pre-training contextual token representations or context-independent sentence/paragraph representations, our hierarchical document representations include fixed-length sentence/paragraph representations which integrate contextual information from the entire documents. Experiments on document segmentation, document-level question answering, and extractive document summarization demonstrate the effectiveness of the proposed pre-training algorithms.