What question did this study set out to answer?

This research aims to enhance question answering and answer verification using AI models.

April 1, 2026Open Access

Omuokdlb at the NTCIR-17 QA Lab-Poliinfo-4 Task

Key Points

This research aims to enhance question answering and answer verification using AI models.
Participated in Question Answering-2 and Answer Verification subtasks
Used BERT for matching question summaries with answer utterances
Employed T5 to generate concise answers
Developed binary classifiers for answer verification using BERT
Achieved effective answer generation through T5
Confirmed effectiveness of BERT-based classifiers in verifying answers

Abstract

The omuokdlb team participated in two subtasks in NTCIR 17 QA Lab-Poliinfo-4: Question Answering-2 and Answer Verification. In Question Answering-2, we use Bidirectional Encoder Representations from Transformers (BERT) to match the question summary and the answer utterances. Then, we generated a summary of the answer to the question by using Text-to-text Transfer Transformer (T5). In Answer Verification, we created binary classifiers using BERT to determine whether or not answers, and we confirmed the effectiveness of the combination of the training data.

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