Yonatan Bisk


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KAT: A Knowledge Augmented Transformer for Vision-and-Language
Liangke Gui | Borui Wang | Qiuyuan Huang | Alexander Hauptmann | Yonatan Bisk | Jianfeng Gao
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The primary focus of recent work with large-scale transformers has been on optimizing the amount of information packed into the model’s parameters. In this work, we ask a complementary question: Can multimodal transformers leverage explicit knowledge in their reasoning? Existing, primarily unimodal, methods have explored approaches under the paradigm of knowledge retrieval followed by answer prediction, but leave open questions about the quality and relevance of the retrieved knowledge used, and how the reasoning processes over implicit and explicit knowledge should be integrated. To address these challenges, we propose a - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result (+6% absolute) on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. Additionally, explicit knowledge integration improves interpretability of model predictions in our analysis.

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Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models
Tejas Srinivasan | Yonatan Bisk
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Numerous works have analyzed biases in vision and pre-trained language models individually - however, less attention has been paid to how these biases interact in multimodal settings. This work extends text-based bias analysis methods to investigate multimodal language models, and analyzes intra- and inter-modality associations and biases learned by these models. Specifically, we demonstrate that VL-BERT (Su et al., 2020) exhibits gender biases, often preferring to reinforce a stereotype over faithfully describing the visual scene. We demonstrate these findings on a controlled case-study and extend them for a larger set of stereotypically gendered entities.


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Grounding ‘Grounding’ in NLP
Khyathi Raghavi Chandu | Yonatan Bisk | Alan W Black
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Dependency Induction Through the Lens of Visual Perception
Ruisi Su | Shruti Rijhwani | Hao Zhu | Junxian He | Xinyu Wang | Yonatan Bisk | Graham Neubig
Proceedings of the 25th Conference on Computational Natural Language Learning

Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar induction. However, as compared to dependency grammars, constituency grammars do not provide a straightforward way to incorporate visual information without enforcing language-specific heuristics. In this paper, we propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based heuristic to jointly learn constituency-structure and dependency-structure grammars. Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50% as compared to state-of-the-art models trained on pure text. Next, we propose an extension of our model that leverages both word concreteness and visual semantic role labels in constituency and dependency parsing. Our experiments show that the proposed extension outperforms the current state-of-the-art visually grounded models in constituency parsing even with a smaller grammar size.

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An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games
Alessandro Suglia | Yonatan Bisk | Ioannis Konstas | Antonio Vergari | Emanuele Bastianelli | Andrea Vanzo | Oliver Lemon
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Guessing games are a prototypical instance of the “learning by interacting” paradigm. This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA). We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL). We evaluate the ability of both procedures to generalise: an in-domain evaluation shows an increased accuracy (+7.79) compared with competitors on the evaluation suite CompGuessWhat?!; a transfer evaluation shows improved performance for VQA on the TDIUC dataset in terms of harmonic average accuracy (+5.31) thanks to more fine-grained object representations learned via SPIEL.


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Experience Grounds Language
Yonatan Bisk | Ari Holtzman | Jesse Thomason | Jacob Andreas | Yoshua Bengio | Joyce Chai | Mirella Lapata | Angeliki Lazaridou | Jonathan May | Aleksandr Nisnevich | Nicolas Pinto | Joseph Turian
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

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Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
Alessandro Suglia | Antonio Vergari | Ioannis Konstas | Yonatan Bisk | Emanuele Bastianelli | Andrea Vanzo | Oliver Lemon
Proceedings of the 28th International Conference on Computational Linguistics

In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly. However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time. This provides an unnatural performance advantage when categories at inference time match those at training time, and it causes models to fail in more realistic “zero-shot” scenarios where out-of-domain object categories are involved. To overcome this issue, we introduce a novel “imagination” module based on Regularized Auto-Encoders, that learns context-aware and category-aware latent embeddings without relying on category labels at inference time. Our imagination module outperforms state-of-the-art competitors by 8.26% gameplay accuracy in the CompGuessWhat?! zero-shot scenario (Suglia et al., 2020), and it improves the Oracle and Guesser accuracy by 2.08% and 12.86% in the GuessWhat?! benchmark, when no gold categories are available at inference time. The imagination module also boosts reasoning about object properties and attributes.

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The Return of Lexical Dependencies: Neural Lexicalized PCFGs
Hao Zhu | Yonatan Bisk | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 8

In this paper we demonstrate that context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies. This contrasts to the most popular current methods for grammar induction, which focus on discovering either constituents or dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g., lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs that allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone.1

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RMM: A Recursive Mental Model for Dialogue Navigation
Homero Roman Roman | Yonatan Bisk | Jesse Thomason | Asli Celikyilmaz | Jianfeng Gao
Findings of the Association for Computational Linguistics: EMNLP 2020

Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use the progress agents make towards the goal as a reinforcement learning reward signal to directly inform not only navigation actions, but also both question and answer generation. We demonstrate that RMM enables better generalization to novel environments. Interlocutor modelling may be a way forward for human-agent RMM where robots need to both ask and answer questions.

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A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
Frank F. Xu | Lei Ji | Botian Shi | Junyi Du | Graham Neubig | Yonatan Bisk | Nan Duan
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.


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Shifting the Baseline: Single Modality Performance on Visual Navigation & QA
Jesse Thomason | Daniel Gordon | Yonatan Bisk
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work often compares against random or majority class baselines, we argue that unimodal approaches better capture and reflect dataset biases and therefore provide an important comparison when assessing the performance of multimodal techniques. We present unimodal ablations on three recent datasets in visual navigation and QA, seeing an up to 29% absolute gain in performance over published baselines.

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Benchmarking Hierarchical Script Knowledge
Yonatan Bisk | Jan Buys | Karl Pichotta | Yejin Choi
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Understanding procedural language requires reasoning about both hierarchical and temporal relations between events. For example, “boiling pasta” is a sub-event of “making a pasta dish”, typically happens before “draining pasta,” and requires the use of omitted tools (e.g. a strainer, sink...). While people are able to choose when and how to use abstract versus concrete instructions, the NLP community lacks corpora and tasks for evaluating if our models can do the same. In this paper, we introduce KidsCook, a parallel script corpus, as well as a cloze task which matches video captions with missing procedural details. Experimental results show that state-of-the-art models struggle at this task, which requires inducing functional commonsense knowledge not explicitly stated in text.

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Robust Navigation with Language Pretraining and Stochastic Sampling
Xiujun Li | Chunyuan Li | Qiaolin Xia | Yonatan Bisk | Asli Celikyilmaz | Jianfeng Gao | Noah A. Smith | Yejin Choi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments. In this paper, we report two simple but highly effective methods to address these challenges and lead to a new state-of-the-art performance. First, we adapt large-scale pretrained language models to learn text representations that generalize better to previously unseen instructions. Second, we propose a stochastic sampling scheme to reduce the considerable gap between the expert actions in training and sampled actions in test, so that the agent can learn to correct its own mistakes during long sequential action decoding. Combining the two techniques, we achieve a new state of the art on the Room-to-Room benchmark with 6% absolute gain over the previous best result (47% -> 53%) on the Success Rate weighted by Path Length metric.

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Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)
Archna Bhatia | Yonatan Bisk | Parisa Kordjamshidi | Jesse Thomason
Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)

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HellaSwag: Can a Machine Really Finish Your Sentence?
Rowan Zellers | Ari Holtzman | Yonatan Bisk | Ali Farhadi | Yejin Choi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as “A woman sits at a piano,” a machine must select the most likely followup: “She sets her fingers on the keys.” With the introduction of BERT, near human-level performance was reached. Does this mean that machines can perform human level commonsense inference? In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%). We achieve this via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers. AF proves to be surprisingly robust. The key insight is to scale up the length and complexity of the dataset examples towards a critical ‘Goldilocks’ zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models. Our construction of HellaSwag, and its resulting difficulty, sheds light on the inner workings of deep pretrained models. More broadly, it suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.


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Proceedings of the Workshop on Generalization in the Age of Deep Learning
Yonatan Bisk | Omer Levy | Mark Yatskar
Proceedings of the Workshop on Generalization in the Age of Deep Learning

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Inducing Grammars with and for Neural Machine Translation
Yonatan Bisk | Ke Tran
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

Machine translation systems require semantic knowledge and grammatical understanding. Neural machine translation (NMT) systems often assume this information is captured by an attention mechanism and a decoder that ensures fluency. Recent work has shown that incorporating explicit syntax alleviates the burden of modeling both types of knowledge. However, requiring parses is expensive and does not explore the question of what syntax a model needs during translation. To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees. In this way, we leverage the benefits of structure while investigating what syntax NMT must induce to maximize performance. We show that our dependency trees are 1. language pair dependent and 2. improve translation quality.

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SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Rowan Zellers | Yonatan Bisk | Roy Schwartz | Yejin Choi
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Given a partial description like “she opened the hood of the car,” humans can reason about the situation and anticipate what might come next (”then, she examined the engine”). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.


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Natural Language Inference from Multiple Premises
Alice Lai | Yonatan Bisk | Julia Hockenmaier
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard textual entailment.

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Proceedings of the First Workshop on Language Grounding for Robotics
Mohit Bansal | Cynthia Matuszek | Jacob Andreas | Yoav Artzi | Yonatan Bisk
Proceedings of the First Workshop on Language Grounding for Robotics


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Evaluating Induced CCG Parsers on Grounded Semantic Parsing
Yonatan Bisk | Siva Reddy | John Blitzer | Julia Hockenmaier | Mark Steedman
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Neural Hidden Markov Models
Ke M. Tran | Yonatan Bisk | Ashish Vaswani | Daniel Marcu | Kevin Knight
Proceedings of the Workshop on Structured Prediction for NLP

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Supertagging With LSTMs
Ashish Vaswani | Yonatan Bisk | Kenji Sagae | Ryan Musa
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Natural Language Communication with Robots
Yonatan Bisk | Deniz Yuret | Daniel Marcu
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Probing the Linguistic Strengths and Limitations of Unsupervised Grammar Induction
Yonatan Bisk | Julia Hockenmaier
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Labeled Grammar Induction with Minimal Supervision
Yonatan Bisk | Christos Christodoulopoulos | Julia Hockenmaier
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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An HDP Model for Inducing Combinatory Categorial Grammars
Yonatan Bisk | Julia Hockenmaier
Transactions of the Association for Computational Linguistics, Volume 1

We introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text. It achieves state of the art performance on a number of languages, and induces linguistically plausible lexicons.


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Induction of Linguistic Structure with Combinatory Categorial Grammars
Yonatan Bisk | Julia Hockenmaier
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure


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Normal-form parsing for Combinatory Categorial Grammars with generalized composition and type-raising
Julia Hockenmaier | Yonatan Bisk
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)