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        <title>cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model</title>
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    <name type="personal">
        <namePart type="given">Shi</namePart>
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            <title>Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion</title>
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        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="given">John</namePart>
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            <namePart type="given">Manel</namePart>
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            <namePart type="given">Kalika</namePart>
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            <namePart type="given">Paul</namePart>
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    <abstract>This paper mainly introduces the relevant content of the task “Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021”. A total of three language datasets were provided, and we chose the English dataset to complete this task. The specific task objective is to classify the given speech into ‘Hope speech’, ‘Not Hope speech’, and ‘Not in intended language’. In terms of method, we use fine-tuned ALBERT and K fold cross-validation to accomplish this task. In the end, we achieved a good result in the rank list of the task result, and the final F1 score was 0.93, tying for first place. However, we will continue to try to improve methods to get better results in future work.</abstract>
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%0 Conference Proceedings
%T cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model
%A Chen, Shi
%A Kong, Bing
%Y Chakravarthi, Bharathi Raja
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F chen-kong-2021-cs-english
%X This paper mainly introduces the relevant content of the task “Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021”. A total of three language datasets were provided, and we chose the English dataset to complete this task. The specific task objective is to classify the given speech into ‘Hope speech’, ‘Not Hope speech’, and ‘Not in intended language’. In terms of method, we use fine-tuned ALBERT and K fold cross-validation to accomplish this task. In the end, we achieved a good result in the rank list of the task result, and the final F1 score was 0.93, tying for first place. However, we will continue to try to improve methods to get better results in future work.
%U https://aclanthology.org/2021.ltedi-1.18/
%P 128-131

Markdown (Informal)

[cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model](https://aclanthology.org/2021.ltedi-1.18/) (Chen & Kong, LTEDI 2021)

• cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model (Chen & Kong, LTEDI 2021)

ACL

• Shi Chen and Bing Kong. 2021. cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, pages 128–131, Kyiv. Association for Computational Linguistics.