Year: 2021
Type: conference-paper
Abstract: Semantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences.Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language.State-of-the-art algorithms usually employ a strongly supervised, resource-rich approach difficult to use for poorly-resourced languages.However, any approach needs to have evaluation data to confirm the results.In order to simplify the evaluation process for poorlyresourced languages (in terms of STS evaluation datasets), we present new datasets for cross-lingual and monolingual STS for languages without this evaluation data.We also present the results of several state-of-the-art methods on these data which can be used as a baseline for further research.We believe that this article will not only extend the current STS research to other languages, but will also encourage competition on this new evaluation data.
Authors: Tomáš Hercig, Pavel Král
Institution: University of West Bohemia in Pilsen
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W3214217520