@inproceedings{chakrabarty-muresan-2019-columbianlp,
title = "{C}olumbia{NLP} at {S}em{E}val-2019 Task 8: The Answer is Language Model Fine-tuning",
author = "Chakrabarty, Tuhin and
Muresan, Smaranda",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2200",
doi = "10.18653/v1/S19-2200",
pages = "1144--1148",
abstract = "Community Question Answering forums are very popular nowadays, as they represent effective means for communities to share information around particular topics. But the information shared on these forums are often not authentic. This paper presents the ColumbiaNLP submission for the SemEval-2019 Task 8: Fact-Checking in Community Question Answering Forums. We show how fine-tuning a language model on a large unannotated corpus of old threads from Qatar Living forum helps us to classify question types (factual, opinion, socializing) and to judge the factuality of answers on the shared task labeled data from the same forum. Our system finished 4th and 2nd on Subtask A (question type classification) and B (answer factuality prediction), respectively, based on the official metric of accuracy.",
}
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<abstract>Community Question Answering forums are very popular nowadays, as they represent effective means for communities to share information around particular topics. But the information shared on these forums are often not authentic. This paper presents the ColumbiaNLP submission for the SemEval-2019 Task 8: Fact-Checking in Community Question Answering Forums. We show how fine-tuning a language model on a large unannotated corpus of old threads from Qatar Living forum helps us to classify question types (factual, opinion, socializing) and to judge the factuality of answers on the shared task labeled data from the same forum. Our system finished 4th and 2nd on Subtask A (question type classification) and B (answer factuality prediction), respectively, based on the official metric of accuracy.</abstract>
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%0 Conference Proceedings
%T ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning
%A Chakrabarty, Tuhin
%A Muresan, Smaranda
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F chakrabarty-muresan-2019-columbianlp
%X Community Question Answering forums are very popular nowadays, as they represent effective means for communities to share information around particular topics. But the information shared on these forums are often not authentic. This paper presents the ColumbiaNLP submission for the SemEval-2019 Task 8: Fact-Checking in Community Question Answering Forums. We show how fine-tuning a language model on a large unannotated corpus of old threads from Qatar Living forum helps us to classify question types (factual, opinion, socializing) and to judge the factuality of answers on the shared task labeled data from the same forum. Our system finished 4th and 2nd on Subtask A (question type classification) and B (answer factuality prediction), respectively, based on the official metric of accuracy.
%R 10.18653/v1/S19-2200
%U https://aclanthology.org/S19-2200
%U https://doi.org/10.18653/v1/S19-2200
%P 1144-1148
Markdown (Informal)
[ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning](https://aclanthology.org/S19-2200) (Chakrabarty & Muresan, SemEval 2019)
ACL