Evaluating cross-lingual textual similarity on dictionary alignment problem

Date

2020-06-01

Author

Sever, Yiğit

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Bilingual or even polylingual word embeddings created many possibilities for tasks involving multiple languages. While some tasks like cross-lingual information retrieval aim to satisfy users' multilingual information needs, some enable transferring valuable information from resource-rich languages to resource-poor ones. In any case, it is important to build and evaluate methods that operate in a cross-lingual setting. In this paper, Wordnet definitions in 7 different languages are used to create a semantic textual similarity testbed to evaluate cross-lingual textual semantic similarity methods. A document alignment task is created to be used between Wordnet glosses of synsets in 7 different languages. Unsupervised textual similarity methods-Wasserstein distance, Sinkhorn distance and cosine similarity-are compared with a supervised Siamese deep learning model. The task is modeled both as a retrieval task and an alignment task to investigate the hubness of the semantic similarity functions. Our findings indicate that considering the problem as a retrieval and alignment problem has a detrimental effect on the results. Furthermore, we show that cross-lingual textual semantic similarity can be used as an automated Wordnet construction method.

Subject Keywords

Linguistics and Language, Education, Library and Information Sciences, Language and Linguistics, Cross-lingual textual semantic similarity, Word embeddings, Wasserstein distance, Sinkhorn distance, Siamese neural network

URI

https://hdl.handle.net/11511/35148

Journal

LANGUAGE RESOURCES AND EVALUATION

DOI

https://doi.org/10.1007/s10579-020-09498-1

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Department of Computer Engineering, Article

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Citation Formats

Y. Sever, “Evaluating cross-lingual textual similarity on dictionary alignment problem,” LANGUAGE RESOURCES AND EVALUATION, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35148.