2 research outputs found
SubjQA: A Dataset for Subjectivity and Review Comprehension
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 therefore investigate the relationship between
subjectivity and QA, while developing a new dataset. We compare and contrast
with analyses from previous work, and verify that findings regarding
subjectivity still hold when using recently developed NLP architectures. We
find that subjectivity is also an important feature in the case of QA, albeit
with more intricate interactions between subjectivity and QA performance. 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
distinct domains.Comment: EMNLP 2020 Long Paper - Camera Read