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        <title>Yet at the FinNLP-2022 ERAI Task: Modified models for evaluating the Rationales of Amateur Investors</title>
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    <name type="personal">
        <namePart type="given">Yan</namePart>
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        <namePart type="given">Fuji</namePart>
        <namePart type="family">Ren</namePart>
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            <title>Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)</title>
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        <name type="personal">
            <namePart type="given">Chung-Chi</namePart>
            <namePart type="family">Chen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Hen-Hsen</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Hiroya</namePart>
            <namePart type="family">Takamura</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Hsin-Hsi</namePart>
            <namePart type="family">Chen</namePart>
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                <placeTerm type="text">Abu Dhabi, United Arab Emirates (Hybrid)</placeTerm>
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    <abstract>The financial reports usually reveal the recent development of the company and often cause the volatility in the company’s share price. The opinions causing higher maximal potential profit and lower maximal loss can help the amateur investors choose rational strategies. FinNLP-2022 ERAI task aims to quantify the opinions’ potentials of leading higher maximal potential profit and lower maximal loss. In this paper, different strategies were applied to solve the ERAI tasks. Valinna ‘RoBERTa-wwm’ showed excellent performance and helped us rank second in ‘MPP’ label prediction task. After integrating some tricks, the modified ‘RoBERTa-wwm’ outperformed all other models in ‘ML’ ranking task.</abstract>
    <identifier type="citekey">zhuang-ren-2022-yet</identifier>
    <identifier type="doi">10.18653/v1/2022.finnlp-1.17</identifier>
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        <url>https://aclanthology.org/2022.finnlp-1.17</url>
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    <part>
        <date>2022-12</date>
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            <start>132</start>
            <end>135</end>
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%0 Conference Proceedings
%T Yet at the FinNLP-2022 ERAI Task: Modified models for evaluating the Rationales of Amateur Investors
%A Zhuang, Yan
%A Ren, Fuji
%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 zhuang-ren-2022-yet
%X The financial reports usually reveal the recent development of the company and often cause the volatility in the company’s share price. The opinions causing higher maximal potential profit and lower maximal loss can help the amateur investors choose rational strategies. FinNLP-2022 ERAI task aims to quantify the opinions’ potentials of leading higher maximal potential profit and lower maximal loss. In this paper, different strategies were applied to solve the ERAI tasks. Valinna ‘RoBERTa-wwm’ showed excellent performance and helped us rank second in ‘MPP’ label prediction task. After integrating some tricks, the modified ‘RoBERTa-wwm’ outperformed all other models in ‘ML’ ranking task.
%R 10.18653/v1/2022.finnlp-1.17
%U https://aclanthology.org/2022.finnlp-1.17
%U https://doi.org/10.18653/v1/2022.finnlp-1.17
%P 132-135

Markdown (Informal)

[Yet at the FinNLP-2022 ERAI Task: Modified models for evaluating the Rationales of Amateur Investors](https://aclanthology.org/2022.finnlp-1.17) (Zhuang & Ren, FinNLP 2022)

• Yet at the FinNLP-2022 ERAI Task: Modified models for evaluating the Rationales of Amateur Investors (Zhuang & Ren, FinNLP 2022)

ACL

• Yan Zhuang and Fuji Ren. 2022. Yet at the FinNLP-2022 ERAI Task: Modified models for evaluating the Rationales of Amateur Investors. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 132–135, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.