Promoting convergence and efficacy of open‐domain question answering via unsupervised clustering

Autor:	Shuoyan Liu, Qiuchi Han
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	question answering (information retrieval) unsupervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Electronics Letters, Vol 60, Iss 16, Pp n/a-n/a (2024)
Druh dokumentu:	article
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.13239
Popis:	Abstract Open‐Domain Question Answering (ODQA) has attracted increasing interests due to its extensive applications in search engines and smart robots. In the experiments, it is observed that the convergence of the method has a huge effect on the generalizability performance. Motivated by this observation, an unsupervised clustering technique (namely, ClusSampling) is proposed to promote both the convergence and efficacy of existing ODQA methods via unsupervised clustering. Specifically, unsupervised clustering is first conducted and then negative samples are selected for higher similarity to the questions. In addition, the authors propose to use gap statistics to determine the optimal number of clusters. Experimental results show that the method achieves notable speedup during training and produces accuracy gains of 5.3% and 2.2 on two widely used benchmarks.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/fa874960cf1944b4b8486ef85fcdffe3 Zobrazit plný text záznamu Plný text View record in DOAJ