@inproceedings{gong-etal-2020-tablegpt,
title = "{T}able{GPT}: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching",
author = "Gong, Heng and
Sun, Yawei and
Feng, Xiaocheng and
Qin, Bing and
Bi, Wei and
Liu, Xiaojiang and
Liu, Ting",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.179",
doi = "10.18653/v1/2020.coling-main.179",
pages = "1978--1988",
abstract = "Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data. Recently, pre-trained language models show potential in few-shot learning with linguistic knowledge learnt from pretraining on large-scale corpus. However, benefiting table-to-text generation in few-shot setting with the powerful pretrained language model faces three challenges, including (1) the gap between the task{'}s structured input and the natural language input for pretraining language model. (2) The lack of modeling for table structure and (3) improving text fidelity with less incorrect expressions that are contradicting to the table. To address aforementioned problems, we propose TableGPT for table-to-text generation. At first, we utilize table transformation module with template to rewrite structured table in natural language as input for GPT-2. In addition, we exploit multi-task learning with two auxiliary tasks that preserve table{'}s structural information by reconstructing the structure from GPT-2{'}s representation and improving the text{'}s fidelity with content matching task aligning the table and information in the generated text. By experimenting on Humans, Songs and Books, three few-shot table-to-text datasets in different domains, our model outperforms existing systems on most few-shot settings.",
}
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<abstract>Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data. Recently, pre-trained language models show potential in few-shot learning with linguistic knowledge learnt from pretraining on large-scale corpus. However, benefiting table-to-text generation in few-shot setting with the powerful pretrained language model faces three challenges, including (1) the gap between the task’s structured input and the natural language input for pretraining language model. (2) The lack of modeling for table structure and (3) improving text fidelity with less incorrect expressions that are contradicting to the table. To address aforementioned problems, we propose TableGPT for table-to-text generation. At first, we utilize table transformation module with template to rewrite structured table in natural language as input for GPT-2. In addition, we exploit multi-task learning with two auxiliary tasks that preserve table’s structural information by reconstructing the structure from GPT-2’s representation and improving the text’s fidelity with content matching task aligning the table and information in the generated text. By experimenting on Humans, Songs and Books, three few-shot table-to-text datasets in different domains, our model outperforms existing systems on most few-shot settings.</abstract>
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%0 Conference Proceedings
%T TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
%A Gong, Heng
%A Sun, Yawei
%A Feng, Xiaocheng
%A Qin, Bing
%A Bi, Wei
%A Liu, Xiaojiang
%A Liu, Ting
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F gong-etal-2020-tablegpt
%X Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data. Recently, pre-trained language models show potential in few-shot learning with linguistic knowledge learnt from pretraining on large-scale corpus. However, benefiting table-to-text generation in few-shot setting with the powerful pretrained language model faces three challenges, including (1) the gap between the task’s structured input and the natural language input for pretraining language model. (2) The lack of modeling for table structure and (3) improving text fidelity with less incorrect expressions that are contradicting to the table. To address aforementioned problems, we propose TableGPT for table-to-text generation. At first, we utilize table transformation module with template to rewrite structured table in natural language as input for GPT-2. In addition, we exploit multi-task learning with two auxiliary tasks that preserve table’s structural information by reconstructing the structure from GPT-2’s representation and improving the text’s fidelity with content matching task aligning the table and information in the generated text. By experimenting on Humans, Songs and Books, three few-shot table-to-text datasets in different domains, our model outperforms existing systems on most few-shot settings.
%R 10.18653/v1/2020.coling-main.179
%U https://aclanthology.org/2020.coling-main.179
%U https://doi.org/10.18653/v1/2020.coling-main.179
%P 1978-1988
Markdown (Informal)
[TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching](https://aclanthology.org/2020.coling-main.179) (Gong et al., COLING 2020)
ACL