Search Results for author: Chandra Bhagavatula

Found 38 papers, 19 papers with code

proScript: Partially Ordered Scripts Generation

no code implementations Findings (EMNLP) 2021 Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, Yejin Choi

Scripts – prototypical event sequences describing everyday activities – have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.

Text Generation

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Text Generation

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

no code implementations24 May 2022 JaeHun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi

Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.

Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions

no code implementations23 May 2022 Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, Kathleen McKeown, Doug Downey, Yejin Choi

However, they are not universally true -- while sparrows and penguins are both birds, only sparrows can fly and penguins cannot.

The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning

no code implementations10 Feb 2022 Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, Yejin Choi

We present Sherlock, an annotated corpus of 103K images for testing machine capacity for abductive reasoning beyond literal image contents.

Visual Reasoning

CommonsenseQA 2.0: Exposing the Limits of AI through Gamification

no code implementations14 Jan 2022 Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant

Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense.

Common Sense Reasoning Natural Language Understanding

Delphi: Towards Machine Ethics and Norms

no code implementations14 Oct 2021 Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Maxwell Forbes, Jon Borchardt, Jenny Liang, Oren Etzioni, Maarten Sap, Yejin Choi

We identify four underlying challenges towards machine ethics and norms: (1) an understanding of moral precepts and social norms; (2) the ability to perceive real-world situations visually or by reading natural language descriptions; (3) commonsense reasoning to anticipate the outcome of alternative actions in different contexts; (4) most importantly, the ability to make ethical judgments given the interplay between competing values and their grounding in different contexts (e. g., the right to freedom of expression vs. preventing the spread of fake news).

``I'm Not Mad'': Commonsense Implications of Negation and Contradiction

no code implementations NAACL 2021 Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, Yejin Choi

In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.

Natural Language Inference

proScript: Partially Ordered Scripts Generation via Pre-trained Language Models

no code implementations16 Apr 2021 Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, Yejin Choi

Scripts - standardized event sequences describing typical everyday activities - have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.

Text Generation

"I'm Not Mad": Commonsense Implications of Negation and Contradiction

no code implementations13 Apr 2021 Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, Yejin Choi

In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.

Natural Language Inference

UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

1 code implementation24 Mar 2021 Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets.

Knowledge Graphs Pretrained Language Models +1

On-the-Fly Attention Modulation for Neural Generation

no code implementations Findings (ACL) 2021 Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi

Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, self-contradictory, and often lacks commonsense.

Language Modelling Text Generation

NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints

no code implementations NAACL 2021 Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples.

Conditional Text Generation Pretrained Language Models

Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models

no code implementations ACL 2021 Peter West, Ximing Lu, Ari Holtzman, Chandra Bhagavatula, Jena Hwang, Yejin Choi

In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks.

Conditional Text Generation Text Infilling

Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

1 code implementation Findings of the Association for Computational Linguistics 2020 Ana Marasović, Chandra Bhagavatula, Jae Sung Park, Ronan Le Bras, Noah A. Smith, Yejin Choi

Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights.

Language Modelling Natural Language Inference +5

COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

no code implementations12 Oct 2020 Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, Yejin Choi

Next, we show that ATOMIC 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events.

Knowledge Graphs Natural Language Understanding +1

Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning

1 code implementation EMNLP 2020 Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi

Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future.

Text Infilling

Paragraph-level Commonsense Transformers with Recurrent Memory

1 code implementation4 Oct 2020 Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz, Ronan Le Bras, Maxwell Forbes, Yejin Choi

Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.

VisualCOMET: Reasoning about the Dynamic Context of a Still Image

no code implementations ECCV 2020 Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin Choi

In addition, we provide person-grounding (i. e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text.

Visual Commonsense Reasoning

Adversarial Filters of Dataset Biases

1 code implementation ICML 2020 Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi

Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples.

Natural Language Inference

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

2 code implementations Findings of the Association for Computational Linguistics 2020 Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, Xiang Ren

In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.

 Ranked #1 on Text Generation on CommonGen (CIDEr metric)

Common Sense Reasoning Question Answering +2

Commonsense Knowledge Base Completion with Structural and Semantic Context

no code implementations7 Oct 2019 Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, Yejin Choi

Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.

Knowledge Base Completion Knowledge Graphs +3

Counterfactual Story Reasoning and Generation

1 code implementation IJCNLP 2019 Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, Yejin Choi

Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes.

Pretrained Language Models Text Generation

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

no code implementations IJCNLP 2019 Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

In this paper, we introduce Cosmos QA, a large-scale dataset of 35, 600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions.

Machine Reading Comprehension Multiple-choice

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

2 code implementations24 Jul 2019 Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi

The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.

Transfer Learning

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

2 code implementations31 Oct 2018 Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge.

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