1.1k citations · 3.7k across the 135 of their papers we have counts for
24 papers · 1 filter
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
Denis Emelin, Ronan Le Bras, Jena D. Hwang +2
In social settings, much of human behavior is governed by unspoken rules of conduct. For artificial systems to be fully integrated into social environments, adherence to such norms…
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Faeze Brahman, Vered Shwartz, Rachel Rudinger +1
The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such ratio…
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
Jeff Da, Maxwell Forbes, Rowan Zellers +4
Multimodal disinformation, from 'deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- s…
Social Chemistry 101: Learning to Reason about Social and Moral Norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz +2
Social norms -- the unspoken commonsense rules about acceptable social behavior -- are crucial in understanding the underlying causes and intents of people's actions in narratives.…
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction
Xinyao Ma, Maarten Sap, Hannah Rashkin +1
Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a stor…
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
Ana Marasović, Chandra Bhagavatula, Jae Sung Park +3
Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanat…
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie +4
Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Ma…
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
Ximing Lu, Peter West, Rowan Zellers +3
Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text…
GO FIGURE: A Meta Evaluation of Factuality in Summarization
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha +2
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This majo…
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models
Peter West, Ximing Lu, Ari Holtzman +3
Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately app…
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras +4
Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense kn…
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Lianhui Qin, Vered Shwartz, Peter West +5
Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple conte…
Paragraph-level Commonsense Transformers with Recurrent Memory
Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz +3
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text. A recent model, COMET, can generate such implicit common…
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Samuel Gehman, Suchin Gururangan, Maarten Sap +2
Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which…
Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes
Nicholas Lourie, Ronan Le Bras, Yejin Choi
As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms. Motivated by descriptive ethics, a f…
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking
Hannah Rashkin, Asli Celikyilmaz, Yejin Choi +1
We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to gener…
Generative Data Augmentation for Commonsense Reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez +6
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive…
VisualCOMET: Reasoning about the Dynamic Context of a Still Image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi +2
Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggli…
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Xiujun Li, Xi Yin, Chunyuan Li +9
Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatena…
Unsupervised Commonsense Question Answering with Self-Talk
Vered Shwartz, Peter West, Ronan Le Bras +2
Natural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implici…
TuringAdvice: A Generative and Dynamic Evaluation of Language Use
Rowan Zellers, Ari Holtzman, Elizabeth Clark +3
We propose TuringAdvice, a new challenge task and dataset for language understanding models. Given a written situation that a real person is currently facing, a model must generate…
Procedural Reading Comprehension with Attribute-Aware Context Flow
Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra +2
Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural…
Multi-View Learning for Vision-and-Language Navigation
Qiaolin Xia, Xiujun Li, Chunyuan Li +5
Learning to navigate in a visual environment following natural language instructions is a challenging task because natural language instructions are highly variable, ambiguous, and…
Adversarial Filters of Dataset Biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula +4
Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution…