Qirong Ho
2024
ActionIE: Action Extraction from Scientific Literature with Programming Languages
Xianrui Zhong
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Yufeng Du
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Siru Ouyang
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Ming Zhong
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Tingfeng Luo
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Qirong Ho
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Hao Peng
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Heng Ji
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Jiawei Han
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Extraction of experimental procedures from human language in scientific literature and patents into actionable sequences in robotics language holds immense significance in scientific domains. Such an action extraction task is particularly challenging given the intricate details and context-dependent nature of the instructions, especially in fields like chemistry where reproducibility is paramount. In this paper, we introduce ActionIE, a method that leverages Large Language Models (LLMs) to bridge this divide by converting actions written in natural language into executable Python code. This enables us to capture the entities of interest, and the relationship between each action, given the features of Programming Languages. Utilizing linguistic cues identified by frequent patterns, ActionIE provides an improved mechanism to discern entities of interest. While our method is broadly applicable, we exemplify its power in the domain of chemical literature, wherein we focus on extracting experimental procedures for chemical synthesis. The code generated by our method can be easily transformed into robotics language which is in high demand in scientific fields. Comprehensive experiments demonstrate the superiority of our method. In addition, we propose a graph-based metric to more accurately reflect the precision of extraction. We also develop a dataset to address the scarcity of scientific literature occurred in existing datasets.
2023
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Chenxu Yang
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Zheng Lin
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Lanrui Wang
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Chong Tian
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Liang Pang
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Jiangnan Li
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Qirong Ho
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Yanan Cao
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Weiping Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context. However, the model often fails to internalize this information into responses in a human-like manner. Instead, it simply inserts segments of the provided knowledge into generic responses. As a result, the generated responses tend to be tedious, incoherent, and in lack of interactivity which means the degeneration problem is still unsolved. In this work, we first find that such copying-style degeneration is primarily due to the weak likelihood objective, which allows the model to “cheat” the objective by merely duplicating knowledge segments in a superficial pattern matching based on overlap. To overcome this challenge, we then propose a Multi-level Adaptive Contrastive Learning (MACL) framework that dynamically samples negative examples and subsequently penalizes degeneration behaviors at both the token-level and sequence-level. Extensive experiments on the WoW dataset demonstrate the effectiveness of our approach across various pre-trained models and decoding strategies.
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Co-authors
- Xianrui Zhong 1
- Yufeng Du 1
- Siru Ouyang 1
- Ming Zhong 1
- Tingfeng Luo 1
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