VERIFICATION OF UNANSWERABLE QUESTIONS IN THE QUESTION ANSWERING SYSTEM USING SENTENCE-BERT AND COSINE SIMILARITY
With the growing quantity of human-machine interaction in modern times, the need for a question answering system is also felt to improve the quality of human- machine interaction. One part of the question answering study is reading comprehension where the system must be able to determine the...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/66650 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | With the growing quantity of human-machine interaction in modern times, the need
for a question answering system is also felt to improve the quality of human-
machine interaction. One part of the question answering study is reading
comprehension where the system must be able to determine the correct answer from
the given document and state if there is no answer in the document. ELECTRA as
previous research is state-of-the-art on the SQuAD 2.0. However, this research can
still be developed again because ELECTRA on the SQuAD 2.0 dev dataset still
makes 1,287 answers errors in determining whether the question is answerable or
unanswerable. This study proposes a modification to ELECTRA in verifying
answers by adding similarity parameters. Sentence-BERT which is a state-of-the-
art in sentence embedding is used to get the sentence similarity between context and
prediction that have been converted into declarative sentences using the rule-based
method. The experimental results show that the proposed method improve the
performance of exact match and F1 scores of ELECTRA. |
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