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        <title>Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions</title>
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    <abstract>We explore the connection between presupposition, discourse and sarcasm and propose to leverage that connection in a transfer learning scenario with the goal of improving the performance of NLI models on cases involving presupposition. We exploit advances in training transformer-based models that show that pre-finetuning—–i.e., finetuning the model on an additional task or dataset before the actual finetuning phase—–can help these models, in some cases, achieve a higher performance on a given downstream task. Building on those advances and that aforementioned connection, we propose pre-finetuning NLI models on carefully chosen tasks in an attempt to improve their performance on NLI cases involving presupposition. We notice that, indeed, pre-finetuning on those tasks leads to performance improvements. Furthermore, we run several diagnostic tests to understand whether these gains are merely a byproduct of additional training data. The results show that, while additional training data seems to be helping on its own in some cases, the choice of the tasks plays a role in the performance improvements.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.703</identifier>
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%0 Conference Proceedings
%T Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions
%A Kabbara, Jad
%A Cheung, Jackie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kabbara-cheung-2023-investigating
%X We explore the connection between presupposition, discourse and sarcasm and propose to leverage that connection in a transfer learning scenario with the goal of improving the performance of NLI models on cases involving presupposition. We exploit advances in training transformer-based models that show that pre-finetuning—–i.e., finetuning the model on an additional task or dataset before the actual finetuning phase—–can help these models, in some cases, achieve a higher performance on a given downstream task. Building on those advances and that aforementioned connection, we propose pre-finetuning NLI models on carefully chosen tasks in an attempt to improve their performance on NLI cases involving presupposition. We notice that, indeed, pre-finetuning on those tasks leads to performance improvements. Furthermore, we run several diagnostic tests to understand whether these gains are merely a byproduct of additional training data. The results show that, while additional training data seems to be helping on its own in some cases, the choice of the tasks plays a role in the performance improvements.
%R 10.18653/v1/2023.findings-emnlp.703
%U https://aclanthology.org/2023.findings-emnlp.703
%U https://doi.org/10.18653/v1/2023.findings-emnlp.703
%P 10482-10494

Markdown (Informal)

[Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions](https://aclanthology.org/2023.findings-emnlp.703) (Kabbara & Cheung, Findings 2023)

• Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions (Kabbara & Cheung, Findings 2023)

ACL

• Jad Kabbara and Jackie Cheung. 2023. Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10482–10494, Singapore. Association for Computational Linguistics.