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        <title>LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control</title>
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        <namePart type="given">Yilun</namePart>
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    <abstract>Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table. This work proposes LoFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation. Experimental results on the LogicNLG dataset demonstrate that LoFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously. Our code is publicly available at https://github.com/Yale-LILY/LoFT.</abstract>
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%0 Conference Proceedings
%T LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
%A Zhao, Yilun
%A Qi, Zhenting
%A Nan, Linyong
%A Flores, Lorenzo Jaime
%A Radev, Dragomir
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F zhao-etal-2023-loft
%X Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table content; 2) Diversity: how to generate multiple sentences that offer different perspectives on the table. This work proposes LoFT, which utilizes logic forms as fact verifiers and content planners to control LT2T generation. Experimental results on the LogicNLG dataset demonstrate that LoFT is the first model that addresses unfaithfulness and lack of diversity issues simultaneously. Our code is publicly available at https://github.com/Yale-LILY/LoFT.
%R 10.18653/v1/2023.eacl-main.40
%U https://aclanthology.org/2023.eacl-main.40
%U https://doi.org/10.18653/v1/2023.eacl-main.40
%P 554-561

Markdown (Informal)

[LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control](https://aclanthology.org/2023.eacl-main.40) (Zhao et al., EACL 2023)

• LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control (Zhao et al., EACL 2023)

ACL

• Yilun Zhao, Zhenting Qi, Linyong Nan, Lorenzo Jaime Flores, and Dragomir Radev. 2023. LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 554–561, Dubrovnik, Croatia. Association for Computational Linguistics.