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        <title>Shallow Discourse Parsing on Twitter Conversations</title>
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        <namePart type="given">Berfin</namePart>
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        <name type="personal">
            <namePart type="given">Harry</namePart>
            <namePart type="family">Bunt</namePart>
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        <name type="personal">
            <namePart type="given">Nancy</namePart>
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        <name type="personal">
            <namePart type="given">Kiyong</namePart>
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        <name type="personal">
            <namePart type="given">James</namePart>
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        <name type="personal">
            <namePart type="given">Laurent</namePart>
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    <abstract>We present our PDTB-style annotations on conversational Twitter data, which was initially annotated by Scheffler et al. (2019). We introduced 1,043 new annotations to the dataset, nearly doubling the number of previously annotated discourse relations. Subsequently, we applied a neural Shallow Discourse Parsing (SDP) model to the resulting corpus, improving its performance through retraining with in-domain data. The most substantial improvement was observed in the sense identification task (+19%). Our experiments with diverse training data combinations underline the potential benefits of exploring various data combinations in domain adaptation efforts for SDP. To the best of our knowledge, this is the first application of Shallow Discourse Parsing on Twitter data</abstract>
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%0 Conference Proceedings
%T Shallow Discourse Parsing on Twitter Conversations
%A Aktas, Berfin
%A Özmen, Burak
%Y Bunt, Harry
%Y Ide, Nancy
%Y Lee, Kiyong
%Y Petukhova, Volha
%Y Pustejovsky, James
%Y Romary, Laurent
%S Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F aktas-ozmen-2024-shallow
%X We present our PDTB-style annotations on conversational Twitter data, which was initially annotated by Scheffler et al. (2019). We introduced 1,043 new annotations to the dataset, nearly doubling the number of previously annotated discourse relations. Subsequently, we applied a neural Shallow Discourse Parsing (SDP) model to the resulting corpus, improving its performance through retraining with in-domain data. The most substantial improvement was observed in the sense identification task (+19%). Our experiments with diverse training data combinations underline the potential benefits of exploring various data combinations in domain adaptation efforts for SDP. To the best of our knowledge, this is the first application of Shallow Discourse Parsing on Twitter data
%U https://aclanthology.org/2024.isa-1.8
%P 60-65

Markdown (Informal)

[Shallow Discourse Parsing on Twitter Conversations](https://aclanthology.org/2024.isa-1.8) (Aktas & Özmen, ISA-WS 2024)

• Shallow Discourse Parsing on Twitter Conversations (Aktas & Özmen, ISA-WS 2024)

ACL

• Berfin Aktas and Burak Özmen. 2024. Shallow Discourse Parsing on Twitter Conversations. In Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024, pages 60–65, Torino, Italia. ELRA and ICCL.