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        <title>How Can a Teacher Make Learning From Sparse Data Softer? Application to Business Relation Extraction</title>
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    <name type="personal">
        <namePart type="given">Nathalie</namePart>
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            <title>Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)</title>
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            <namePart type="given">Chung-Chi</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>Business Relation Extraction between market entities is a challenging information extraction task that suffers from data imbalance due to the over-representation of negative relations (also known as No-relation or Others) compared to positive relations that corresponds to the taxonomy of relations of interest. This paper proposes a novel solution to tackle this problem, relying on binary soft labels supervision generated by an approach based on knowledge distillation. When evaluated on a business relation extraction dataset, the results suggest that the proposed approach improves the overall performance, beating state-of-the art solutions for data imbalance. In particular, it improves the extraction of under-represented relations as well as the detection of false negatives.</abstract>
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%0 Conference Proceedings
%T How Can a Teacher Make Learning From Sparse Data Softer? Application to Business Relation Extraction
%A Khaldi, Hadjer
%A Benamara, Farah
%A Pradel, Camille
%A Aussenac-Gilles, Nathalie
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F khaldi-etal-2022-teacher
%X Business Relation Extraction between market entities is a challenging information extraction task that suffers from data imbalance due to the over-representation of negative relations (also known as No-relation or Others) compared to positive relations that corresponds to the taxonomy of relations of interest. This paper proposes a novel solution to tackle this problem, relying on binary soft labels supervision generated by an approach based on knowledge distillation. When evaluated on a business relation extraction dataset, the results suggest that the proposed approach improves the overall performance, beating state-of-the art solutions for data imbalance. In particular, it improves the extraction of under-represented relations as well as the detection of false negatives.
%R 10.18653/v1/2022.finnlp-1.23
%U https://aclanthology.org/2022.finnlp-1.23
%U https://doi.org/10.18653/v1/2022.finnlp-1.23
%P 170-177

Markdown (Informal)

[How Can a Teacher Make Learning From Sparse Data Softer? Application to Business Relation Extraction](https://aclanthology.org/2022.finnlp-1.23) (Khaldi et al., FinNLP 2022)

• How Can a Teacher Make Learning From Sparse Data Softer? Application to Business Relation Extraction (Khaldi et al., FinNLP 2022)

ACL

• Hadjer Khaldi, Farah Benamara, Camille Pradel, and Nathalie Aussenac-Gilles. 2022. How Can a Teacher Make Learning From Sparse Data Softer? Application to Business Relation Extraction. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 170–177, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.