@inproceedings{chaturvedi-asher-2024-learning,
title = "Learning Semantic Structure through First-Order-Logic Translation",
author = "Chaturvedi, Akshay and
Asher, Nicholas",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.390/",
doi = "10.18653/v1/2024.findings-emnlp.390",
pages = "6669--6680",
abstract = "In this paper, we study whether transformer-based language models can extract predicate argument structure from simple sentences. We firstly show that language models sometimes confuse which predicates apply to which objects. To mitigate this, we explore two tasks: question answering (Q/A), and first order logic (FOL) translation, and two regimes, prompting and finetuning. In FOL translation, we finetune several large language models on synthetic datasets designed to gauge their generalization abilities. For Q/A, we finetune encoder models like BERT and RoBERTa and use prompting for LLMs. The results show that FOL translation for LLMs is better suited to learn predicate argument structure."
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%0 Conference Proceedings
%T Learning Semantic Structure through First-Order-Logic Translation
%A Chaturvedi, Akshay
%A Asher, Nicholas
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chaturvedi-asher-2024-learning
%X In this paper, we study whether transformer-based language models can extract predicate argument structure from simple sentences. We firstly show that language models sometimes confuse which predicates apply to which objects. To mitigate this, we explore two tasks: question answering (Q/A), and first order logic (FOL) translation, and two regimes, prompting and finetuning. In FOL translation, we finetune several large language models on synthetic datasets designed to gauge their generalization abilities. For Q/A, we finetune encoder models like BERT and RoBERTa and use prompting for LLMs. The results show that FOL translation for LLMs is better suited to learn predicate argument structure.
%R 10.18653/v1/2024.findings-emnlp.390
%U https://aclanthology.org/2024.findings-emnlp.390/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.390
%P 6669-6680
Markdown (Informal)
[Learning Semantic Structure through First-Order-Logic Translation](https://aclanthology.org/2024.findings-emnlp.390/) (Chaturvedi & Asher, Findings 2024)
ACL