@inproceedings{weir-etal-2024-enhancing,
title = "Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic",
author = "Weir, Nathaniel and
Sanders, Kate and
Weller, Orion and
Sharma, Shreya and
Jiang, Dongwei and
Jiang, Zhengping and
Dalvi Mishra, Bhavana and
Tafjord, Oyvind and
Jansen, Peter and
Clark, Peter and
Van Durme, Benjamin",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.531",
pages = "9458--9482",
abstract = "Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what {\_}valid decompositional entailment{\_} is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.",
}
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<abstract>Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what _valid decompositional entailment_ is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.</abstract>
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%0 Conference Proceedings
%T Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
%A Weir, Nathaniel
%A Sanders, Kate
%A Weller, Orion
%A Sharma, Shreya
%A Jiang, Dongwei
%A Jiang, Zhengping
%A Dalvi Mishra, Bhavana
%A Tafjord, Oyvind
%A Jansen, Peter
%A Clark, Peter
%A Van Durme, Benjamin
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F weir-etal-2024-enhancing
%X Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what _valid decompositional entailment_ is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.
%U https://aclanthology.org/2024.emnlp-main.531
%P 9458-9482
Markdown (Informal)
[Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://aclanthology.org/2024.emnlp-main.531) (Weir et al., EMNLP 2024)
ACL
- Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, and Benjamin Van Durme. 2024. Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9458–9482, Miami, Florida, USA. Association for Computational Linguistics.