SubjQA: A Dataset for Subjectivity and Review Comprehension
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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SubjQA : A Dataset for Subjectivity and Review Comprehension. / Bjerva, Johannes; Bhutani, Nikita; Golshan, Behzad; Tan, Wang-chiew; Augenstein, Isabelle.
SUBJQA: A Dataset for Subjectivity and Review Comprehension. Association for Computational Linguistics, 2020. s. 5480-5494.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - SubjQA
T2 - The 2020 Conference on Empirical Methods in Natural Language Processing
AU - Bjerva, Johannes
AU - Bhutani, Nikita
AU - Golshan, Behzad
AU - Tan, Wang-chiew
AU - Augenstein, Isabelle
PY - 2020
Y1 - 2020
N2 - Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We develop a new dataset which allows us to investigate this relationship. We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.
AB - Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We develop a new dataset which allows us to investigate this relationship. We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.
U2 - 10.18653/v1/2020.emnlp-main.442
DO - 10.18653/v1/2020.emnlp-main.442
M3 - Article in proceedings
SP - 5480
EP - 5494
BT - SUBJQA: A Dataset for Subjectivity and Review Comprehension
PB - Association for Computational Linguistics
Y2 - 16 November 2020 through 20 November 2020
ER -
ID: 254991810