Comparative Analysis of Retriever and Reader for Open Domain Questions Answering on BPS Knowledge in Indonesian

Sulisetyo Puji Widodo
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Abstract

Enumerators from Badan Pusat Statistik (BPS) still often encounter problems in finding solutions to cases encountered during censuses or surveys. Even though knowledge lists have been created and collected in various systems such as QA and knowledge management systems, enumerators still need to find appropriate answers from long and complex knowledge search results. On the other hand, Open-domain Question Answering (OpenQA) is capable of identifying answers to natural questions based on large-scale documents. OpenQA has main components, namely Retriever and Reader. For Retriever tasks, Dense Retrieval (DR) is proven to outperform traditional sparse retrieval such as TF-IDF or BM25. However, other research actually shows that BM25 is superior to DR in terms of accuracy. In this study, we compared DR and BM25 separately and DR+BM25 as a retriever. Additionally, we combine and evaluate several enhanced language models as Readers. In this way, a model with the best combination of Retriever and Reader can be obtained to be implemented in search systems such as QA and knowledge management systems.
检索器和阅读器在印尼语 BPS 知识开放域问题解答中的比较分析
Badan Pusat Statistik(BPS)的统计员在为人口普查或调查过程中遇到的问题寻找解决方案时仍经常遇到困难。尽管各种系统(如 QA 和知识管理系统)已创建并收集了知识列表,但普查员仍需要从冗长而复杂的知识搜索结果中找到适当的答案。另一方面,开放域问题解答(Open-domain Question Answering,简称 OpenQA)能够在大规模文档的基础上识别自然问题的答案。OpenQA 主要由 Retriever 和 Reader 两部分组成。对于 Retriever 任务,密集检索(DR)被证明优于传统的稀疏检索,如 TF-IDF 或 BM25。然而,其他研究实际上表明,BM25 在准确性方面优于 DR。在本研究中,我们分别比较了 DR 和 BM25,以及作为检索器的 DR+BM25。此外,我们还将几个增强型语言模型作为读取器进行了组合和评估。通过这种方法,我们可以得到一个将检索器和阅读器结合得最好的模型,并将其应用于质量保证和知识管理系统等搜索系统中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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