Evaluation Datasets for Cross-lingual Semantic Textual Similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963751" target="_blank" >RIV/49777513:23520/21:43963751 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2021.ranlp-main.59.pdf" target="_blank" >https://aclanthology.org/2021.ranlp-main.59.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26615/978-954-452-072-4_059" target="_blank" >10.26615/978-954-452-072-4_059</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation Datasets for Cross-lingual Semantic Textual Similarity

Popis výsledku v původním jazyce

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 poorly-resourced 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.

Název v anglickém jazyce

Evaluation Datasets for Cross-lingual Semantic Textual Similarity

Popis výsledku anglicky

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 poorly-resourced 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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Deep Learning for Natural Language Processing Methods and Applications

ISBN

978-954-452-072-4

ISSN

1313-8502

e-ISSN

—

Počet stran výsledku

6

Strana od-do

524-529

Název nakladatele

INCOMA, Ltd.

Místo vydání

Shoumen

Místo konání akce

Shoumen, Bulgaria

Datum konání akce

1. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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