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        <title>A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering</title>
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        <namePart type="given">Maria</namePart>
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            <namePart type="family">Che</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training down stream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.</abstract>
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%0 Conference Proceedings
%T A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering
%A Maufe, Matt
%A Ravenscroft, James
%A Procter, Rob
%A Liakata, Maria
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F maufe-etal-2022-pipeline
%X Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training down stream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.
%R 10.18653/v1/2022.emnlp-demos.9
%U https://aclanthology.org/2022.emnlp-demos.9
%U https://doi.org/10.18653/v1/2022.emnlp-demos.9
%P 80-97

Markdown (Informal)

[A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering](https://aclanthology.org/2022.emnlp-demos.9) (Maufe et al., EMNLP 2022)

• A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering (Maufe et al., EMNLP 2022)

ACL

• Matt Maufe, James Ravenscroft, Rob Procter, and Maria Liakata. 2022. A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 80–97, Abu Dhabi, UAE. Association for Computational Linguistics.