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        <title>Text-in-Context: Token-Level Error Detection for Table-to-Text Generation</title>
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        <namePart type="given">Zdeněk</namePart>
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            <title>Proceedings of the 14th International Conference on Natural Language Generation</title>
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            <namePart type="given">Anya</namePart>
            <namePart type="family">Belz</namePart>
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            <namePart type="given">Angela</namePart>
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            <namePart type="given">Ehud</namePart>
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    <abstract>We present our Charles-UPF submission for the Shared Task on Evaluating Accuracy in Generated Texts at INLG 2021. Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs). We first utilize a rule-based NLG system to generate sentences with facts that can be derived from the input. For each sentence we evaluate, we select a subset of facts which are relevant by measuring semantic similarity to the sentence in question. Finally, we finetune a pretrained language model on annotated data along with the relevant facts for fine-grained error detection. On the test set, we achieve 69% recall and 75% precision with a model trained on a mixture of human-annotated and synthetic data.</abstract>
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%0 Conference Proceedings
%T Text-in-Context: Token-Level Error Detection for Table-to-Text Generation
%A Kasner, Zdeněk
%A Mille, Simon
%A Dušek, Ondřej
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F kasner-etal-2021-text
%X We present our Charles-UPF submission for the Shared Task on Evaluating Accuracy in Generated Texts at INLG 2021. Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs). We first utilize a rule-based NLG system to generate sentences with facts that can be derived from the input. For each sentence we evaluate, we select a subset of facts which are relevant by measuring semantic similarity to the sentence in question. Finally, we finetune a pretrained language model on annotated data along with the relevant facts for fine-grained error detection. On the test set, we achieve 69% recall and 75% precision with a model trained on a mixture of human-annotated and synthetic data.
%R 10.18653/v1/2021.inlg-1.25
%U https://aclanthology.org/2021.inlg-1.25
%U https://doi.org/10.18653/v1/2021.inlg-1.25
%P 259-265

Markdown (Informal)

[Text-in-Context: Token-Level Error Detection for Table-to-Text Generation](https://aclanthology.org/2021.inlg-1.25) (Kasner et al., INLG 2021)

• Text-in-Context: Token-Level Error Detection for Table-to-Text Generation (Kasner et al., INLG 2021)

ACL

• Zdeněk Kasner, Simon Mille, and Ondřej Dušek. 2021. Text-in-Context: Token-Level Error Detection for Table-to-Text Generation. In Proceedings of the 14th International Conference on Natural Language Generation, pages 259–265, Aberdeen, Scotland, UK. Association for Computational Linguistics.