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        <title>TINA: Textual Inference with Negation Augmentation</title>
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    <name type="personal">
        <namePart type="given">Chadi</namePart>
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    <name type="personal">
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        <namePart type="family">Clavel</namePart>
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    <name type="personal">
        <namePart type="given">Fabian</namePart>
        <namePart type="family">Suchanek</namePart>
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            <namePart type="given">Yoav</namePart>
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            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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        <name type="personal">
            <namePart type="given">Yue</namePart>
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    <abstract>Transformer-based language models achieve state-of-the-art results on several natural language processing tasks. One of these is textual entailment, i.e., the task of determining whether a premise logically entails a hypothesis. However, the models perform poorly on this task when the examples contain negations. In this paper, we propose a new definition of textual entailment that captures also negation. This allows us to develop TINA (Textual Inference with Negation Augmentation), a principled technique for negated data augmentation that can be combined with the unlikelihood loss function.Our experiments with different transformer-based models show that our method can significantly improve the performance of the models on textual entailment datasets with negation – without sacrificing performance on datasets without negation.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.301</identifier>
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        <date>2022-12</date>
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%0 Conference Proceedings
%T TINA: Textual Inference with Negation Augmentation
%A Helwe, Chadi
%A Coumes, Simon
%A Clavel, Chloé
%A Suchanek, Fabian
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F helwe-etal-2022-tina
%X Transformer-based language models achieve state-of-the-art results on several natural language processing tasks. One of these is textual entailment, i.e., the task of determining whether a premise logically entails a hypothesis. However, the models perform poorly on this task when the examples contain negations. In this paper, we propose a new definition of textual entailment that captures also negation. This allows us to develop TINA (Textual Inference with Negation Augmentation), a principled technique for negated data augmentation that can be combined with the unlikelihood loss function.Our experiments with different transformer-based models show that our method can significantly improve the performance of the models on textual entailment datasets with negation – without sacrificing performance on datasets without negation.
%R 10.18653/v1/2022.findings-emnlp.301
%U https://aclanthology.org/2022.findings-emnlp.301/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.301
%P 4086-4099

Markdown (Informal)

[TINA: Textual Inference with Negation Augmentation](https://aclanthology.org/2022.findings-emnlp.301/) (Helwe et al., Findings 2022)

• TINA: Textual Inference with Negation Augmentation (Helwe et al., Findings 2022)

ACL

• Chadi Helwe, Simon Coumes, Chloé Clavel, and Fabian Suchanek. 2022. TINA: Textual Inference with Negation Augmentation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4086–4099, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.