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[4J3-GS-6f-03] Developing and Evaluating a Context Dataset for How-to Tip Machine Reading Comprehension
Keywords:question answering, machine comprehension, tip, BERT, context
In this paper, we focus on the task of how-to tip machine reading
comprehension (MRC), which is in the field of non-factoid MRC. Then, in
the field of how-to tip MRC, we propose a method to build a context
dataset, to which we apply a certain procedure of retrieving candidates
of context paragraphs that are supposed to include candidates of answers
to the given question. The information source of the context dataset is
the column pages collected from how-to tip Web sites. We show that it is
easy to develop a context dataset consisting of more than a few thousand
context paragraphs. Then, we propose a procedure to combine a search
module based on TF-IDF and a BERT machine reading comprehension model
that is evaluated based on the context dataset developed in this paper.
comprehension (MRC), which is in the field of non-factoid MRC. Then, in
the field of how-to tip MRC, we propose a method to build a context
dataset, to which we apply a certain procedure of retrieving candidates
of context paragraphs that are supposed to include candidates of answers
to the given question. The information source of the context dataset is
the column pages collected from how-to tip Web sites. We show that it is
easy to develop a context dataset consisting of more than a few thousand
context paragraphs. Then, we propose a procedure to combine a search
module based on TF-IDF and a BERT machine reading comprehension model
that is evaluated based on the context dataset developed in this paper.
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