Papers by Larry Birnbaum
Extracting Commonsense Properties from Embeddings with Limited Human Guidance (P18-2)
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| Challenge: | Existing methods for learning common sense from text require dozens of hand-annotated frames to connect the property to how it is indirectly reflected in text. |
| Approach: | They propose a method for extracting object-property comparisons from pre-trained embeddings. |
| Outcome: | The proposed approach exceeds previous work but requires less hand-annotated knowledge. |
Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models (2022.findings-emnlp)
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| Challenge: | Recent work on how to encode compositional task structure has been limited by semantic parsing and multihop reasoning for the purpose of Q&A. |
| Approach: | They propose an approach to decomposing a target task into component tasks and fine-tuning smaller LMs on a curriculum of such component tasks. |
| Outcome: | The proposed approach outperforms end-to-end learning even with equal data, and gets better as more component tasks are modeled. |
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems (2021.emnlp-main)
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| Challenge: | Conversational recommendation systems (CRSs) aim to refine options over multiple turns of a conversation, but they are not as flexible as real conversations. |
| Approach: | They propose a method for transforming a user critique into a positive preference . they use a large neural language model to perform critique-to-preference transformation . |
| Outcome: | The proposed method improves recommendations in restaurant domain using a new dataset of restaurant critiques. |