Abstract:
This paper presents an Open-Structure Table Extraction (OpenTE) task, which aims to extract a table with intrinsic semantic, calculational, and hierarchical structure fro...Show MoreMetadata
Abstract:
This paper presents an Open-Structure Table Extraction (OpenTE) task, which aims to extract a table with intrinsic semantic, calculational, and hierarchical structure from unstructured text. We devise a novel Identification-Extraction-Grounding (IEG) framework for language models (LMs) comprising three chaining steps: (1) identifying semantic and calculational relationships among columns, (2) extracting structured data from unstructured text, and (3) aligning extracted data with the source text and the table structure with a separate discrete grounding model. Experiment results suggest that OpenTE presents a significant challenge for state-of-the-art LMs and demonstrate that the IEG framework achieves superior performance on both datasets, with over 9% F1 improvements in the few-shot setting for GPT-3.5&4 and other large language models (LLMs) and over 4.9% F1 enhancements in the fine-tuning setting for open-source BART. We’ll release the dataset to facilitate future research.
Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 14-19 April 2024
Date Added to IEEE Xplore: 18 March 2024
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