Papers by Rowan Zellers
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)
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| Challenge: | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. |
| Approach: | They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints. |
| Outcome: | The proposed algorithm outperforms existing methods on four benchmarks and shows that it handles any set of lexical constraints expressible under predicate logic while its asymptotic runtime is equivalent to conventional beam search. |
Probing Contextual Language Models for Common Ground with Visual Representations (2021.naacl-main)
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| Challenge: | Contextual language models have attracted great interest in probing what is encoded in their representations. |
| Approach: | They propose a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. |
| Outcome: | The proposed model outperforms text-only language models in instance retrieval, but underperform humans. |
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (D18-1)
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| Challenge: | a new dataset presents a task of grounded commonsense inference, unifying natural language inference and commonsensical reasoning. |
| Approach: | They propose a procedure that constructs a de-biased dataset by iteratively training stylistic classifiers and using them to filter the data. |
| Outcome: | The proposed procedure oversamples a de-biased dataset using state-of-the-art language models . human models struggle on the proposed procedure, indicating significant opportunities for future research. |
HellaSwag: Can a Machine Really Finish Your Sentence? (P19-1)
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| Challenge: | Existing commonsense models struggle to perform inferences that are trivial for humans, but are often misclassified by state-of-the-art models. |
| Approach: | They propose a dataset that is adversarial to state-of-the-art commonsense reasoning and use it to build a model that is surprisingly robust. |
| Outcome: | The proposed dataset is compared with existing models and scaled up towards a critical 'Goldilocks zone' wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models. |
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer (2022.naacl-main)
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| Challenge: | Existing methods for learning audio-text connections rely on parallel audio- text data . a new approach allows for the representation of environmental soundscapes without using parallel data - a challenge for many applications . |
| Approach: | They propose a model that induces Audio-Text alignment without using parallel audio-text data. |
| Outcome: | The proposed model outperforms the current state-of-the-art for audio classification tasks with no audio-text data by 2.2% on the ESC50 and US8K tasks. |
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest (2023.acl-long)
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Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi
| Challenge: | Large neural networks can generate jokes, but do they really “understand” humor? a new challenge challenges AI models to match a joke to a cartoon, identify a winning caption, and explain why a winner is funny. |
| Approach: | They propose three tasks based on the New Yorker Cartoon Caption Contest . they aim to match a joke to a cartoon, identify a winning caption and explain why it's funny . |
| Outcome: | The proposed tasks are based on the New Yorker Cartoon Caption Contest . they include matching a joke to a cartoon, identifying a winning caption, and explaining why a funny caption is funny. |
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (2021.naacl-main)
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| Challenge: | Empirical results show that today’s language models struggle at TuringAdvice . language models are getting ever-larger, and are being trained on ever-increasing quantities of text . |
| Approach: | They propose a task task that requires models to generate helpful advice in natural language. |
| Outcome: | The proposed model outperforms even multibillion parameter models on 600k in-domain training examples. |
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World (2021.acl-long)
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Rowan Zellers, Ari Holtzman, Matthew Peters, Roozbeh Mottaghi, Aniruddha Kembhavi, Ali Farhadi, Yejin Choi
| Challenge: | PIGLeT model learns physical commonsense knowledge through interaction, then uses this knowledge to ground language. |
| Approach: | They propose a model that learns physical commonsense knowledge through interaction . they factorize PIGLeT into a physical dynamics model and a separate language model . |
| Outcome: | The proposed model outperforms a 100x larger, text-to-text approach in forecasting language . it can read a sentence, simulate neurally what might happen next, and communicate that result through a literal symbolic representation, or natural language. |
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation (2021.acl-long)
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| Challenge: | Edited media frames are structured annotations with respect to intents, emotional reactions, attacks on individuals, and the implications of disinformation. |
| Approach: | They propose a new formalism to understand visual media manipulation as structured annotations with respect to intents, emotional reactions, attacks on individuals, and the implications of disinformation. |
| Outcome: | The proposed model obtains promising results on a dataset with 56k question-answer pairs written in rich natural language. |
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2022.naacl-main)
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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
| Challenge: | Existing paradigms for text generation are left-to-right decoding from autoregressive language models. |
| Approach: | They propose a decoding algorithm that incorporates heuristic estimates of future cost that are efficient for large-scale language models. |
| Outcome: | The proposed method outperforms baselines on five generation tasks and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. |