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        <title>MNLP at FinCausal2022: Nested NER with a Generative Model</title>
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        <namePart type="given">Jooyeon</namePart>
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        <namePart type="given">Luan</namePart>
        <namePart type="given">Huy</namePart>
        <namePart type="family">Pham</namePart>
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            <title>Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022</title>
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        <name type="personal">
            <namePart type="given">Mahmoud</namePart>
            <namePart type="family">El-Haj</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Paul</namePart>
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        <name type="personal">
            <namePart type="given">Nadhem</namePart>
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    <abstract>This paper describes work performed for the FinCasual 2022 Shared Task “Financial Document Causality Detection” (FinCausal 2022). As the name implies, the task involves extraction of casual and consequential elements from financial text. Our approach focuses employing Nested NER using the Text-to-Text Transformer (T5) generative transformer models while applying different combinations of datasets and tagging methods. Our system reports accuracy of 79% in Exact Match comparison and F-measure score of 92% token level measurement.</abstract>
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%0 Conference Proceedings
%T MNLP at FinCausal2022: Nested NER with a Generative Model
%A Lee, Jooyeon
%A Pham, Luan Huy
%A Uzuner, Özlem
%Y El-Haj, Mahmoud
%Y Rayson, Paul
%Y Zmandar, Nadhem
%S Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F lee-etal-2022-mnlp
%X This paper describes work performed for the FinCasual 2022 Shared Task “Financial Document Causality Detection” (FinCausal 2022). As the name implies, the task involves extraction of casual and consequential elements from financial text. Our approach focuses employing Nested NER using the Text-to-Text Transformer (T5) generative transformer models while applying different combinations of datasets and tagging methods. Our system reports accuracy of 79% in Exact Match comparison and F-measure score of 92% token level measurement.
%U https://aclanthology.org/2022.fnp-1.24
%P 135-138

Markdown (Informal)

[MNLP at FinCausal2022: Nested NER with a Generative Model](https://aclanthology.org/2022.fnp-1.24) (Lee et al., FNP 2022)

• MNLP at FinCausal2022: Nested NER with a Generative Model (Lee et al., FNP 2022)

ACL

• Jooyeon Lee, Luan Huy Pham, and Özlem Uzuner. 2022. MNLP at FinCausal2022: Nested NER with a Generative Model. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 135–138, Marseille, France. European Language Resources Association.