Papers by Jeff Da
Discourse Understanding and Factual Consistency in Abstractive Summarization (2021.eacl-main)
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Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
| Challenge: | Existing abstractive summarization models often hallucinate information or generate factually incorrect summaries. |
| Approach: | They propose a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. |
| Outcome: | The proposed framework generates abstracts with factual consistency and coherence significantly better than baselines. |
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. |
Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge (D19-60)
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| Challenge: | Recent studies show that large-scale pre-training models can be effective for large datasets. |
| Approach: | They propose a method of integrating contextual embeddings with commonsense graph embeddINGs by preprocessing knowledge bases and aligning tokens between misaligned tokenization methods. |
| Outcome: | The proposed method achieves higher accuracy than BERT and scores highest without pretraining. |
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
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| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
| Outcome: | The proposed model improves performance on a downstream commonsense reasoning task while using minimal data. |
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. |