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        <title>ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction</title>
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        <namePart type="given">Abir</namePart>
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    <name type="personal">
        <namePart type="given">Lipika</namePart>
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            <title>Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022</title>
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            <namePart type="given">Mahmoud</namePart>
            <namePart type="family">El-Haj</namePart>
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            <namePart type="given">Paul</namePart>
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            <namePart type="given">Nadhem</namePart>
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    <abstract>Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on restricted domain data sets. With the advent of big data, and the recent popularization of deep learning, the paradigm to tackle this problem has slowly shifted. In this work we proposed a transformer based architecture to automatically detect causal sentences from textual mentions and then identify the corresponding cause-effect relations. We describe our submission to the FinCausal 2022 shared task based on this method. Our model achieves a F1-score of 0.99 for the Task-1 and F1-score of 0.60 for Task-2 on the shared task data set on financial documents.</abstract>
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        <date>2022-06</date>
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            <start>131</start>
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%0 Conference Proceedings
%T ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction
%A Naskar, Abir
%A Dasgupta, Tirthankar
%A Jana, Sudeshna
%A Dey, Lipika
%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 naskar-etal-2022-atl
%X Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on restricted domain data sets. With the advent of big data, and the recent popularization of deep learning, the paradigm to tackle this problem has slowly shifted. In this work we proposed a transformer based architecture to automatically detect causal sentences from textual mentions and then identify the corresponding cause-effect relations. We describe our submission to the FinCausal 2022 shared task based on this method. Our model achieves a F1-score of 0.99 for the Task-1 and F1-score of 0.60 for Task-2 on the shared task data set on financial documents.
%U https://aclanthology.org/2022.fnp-1.23
%P 131-134

Markdown (Informal)

[ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction](https://aclanthology.org/2022.fnp-1.23) (Naskar et al., FNP 2022)

• ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction (Naskar et al., FNP 2022)

ACL

• Abir Naskar, Tirthankar Dasgupta, Sudeshna Jana, and Lipika Dey. 2022. ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 131–134, Marseille, France. European Language Resources Association.