PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation

Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, Dongmei Zhang

Abstract
Logical table-to-text generation is a task that involves generating logically faithful sentences from tables, which requires models to derive logical-level facts from table records via logical inference. It raises a new challenge on the logical-level content planning of table-to-text models. However, directly learning the logical inference knowledge from table-text pairs is very difficult for neural models because of the ambiguity of natural language and the scarcity of parallel data. Hence even large-scale pre-trained language models present low logical fidelity on logical table-to-text. In this work, we propose a Pretrained Logical Form Generator (PLOG) framework to improve generation fidelity. Specifically, PLOG is first pretrained on a table-to-logical-form generation (table-to-logic) task, then finetuned on downstream table-to-text tasks. The logical forms are formally defined with unambiguous semantics. Hence we can collect a large amount of accurate logical forms from tables without human annotation. In addition, PLOG can learn logical inference from table-logic pairs much more reliably than from table-text pairs. To evaluate our model, we further collect a controlled logical table-to-text dataset CONTLOG based on an existing dataset. On two benchmarks, LOGICNLG and CONTLOG, PLOG outperforms strong baselines by a large margin on the logical fidelity, demonstrating the effectiveness of table-to-logic pretraining.
Anthology ID:
2022.emnlp-main.373
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5531–5546
Language:
URL:
https://aclanthology.org/2022.emnlp-main.373
DOI:
10.18653/v1/2022.emnlp-main.373
Bibkey:
Cite (ACL):
Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, and Dongmei Zhang. 2022. PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5531–5546, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation (Liu et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.373.pdf
PDF Search