Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing datasets like SQuAD-TR and XQuAD-TR, treating them as if they were constructed within a conversational context. Our findings indicate that incorporating conversation signals in the retriever models results in up to an absolute increase of 18.82% in Success@1 for retrievers. This improvement extends to the reader models enhanced by the conversational context, narrowing the gap in EM/F1 scores up to 4.12% / 4.43%, respectively, compared to Standard QA readers.
DOI
10.55730/1300-0632.4122
Keywords
Open-domain question answering, conversational open-domain question answering, low-resource languages, machine translation
First Page
203
Last Page
223
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
BUDUR, EMRAH and GÜNGÖR, TUNGA
(2025)
"Conversational open-domain question answering for resource-constrained languages,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 33:
No.
2, Article 8.
https://doi.org/10.55730/1300-0632.4122
Available at:
https://journals.tubitak.gov.tr/elektrik/vol33/iss2/8
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
