Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations

Johannes Bjerva
Center for Language and Cognition Groningen, University of Groningen, The Netherlands

Robert Ö stling
Department of Linguistics, Stockholm University, Sweden

Ladda ner artikel

Ingår i: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Linköping Electronic Conference Proceedings 131:24, s. 211-215

NEALT Proceedings Series 29:24, s. 211-215

Visa mer +

Publicerad: 2017-05-08

ISBN: 978-91-7685-601-7

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Assessing the semantic similarity between sentences in different languages is challenging. We approach this problem by leveraging multilingual distributional word representations, where similar words in different languages are close to each other. The availability of parallel data allows us to train such representations on a large amount of languages. This allows us to leverage semantic similarity data for languages for which no such data exists. We train and evaluate on five language pairs, including English, Spanish, and Arabic. We are able to train wellperforming systems for several language pairs, without any labelled data for that language pair.


Inga nyckelord är tillgängliga


Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2016. Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation. In Proceedings of SemEval, pages 497–511.

Rami Al-Rfou, Bryan Perozzi, and Steven Skiena. 2013. Polyglot: Distributed word representations for multilingual nlp. CoNLL-2013.

Hanan Aldarmaki and Mona Diab. 2016. GWU NLP at SemEval-2016 Shared Task 1: Matrix factorization for crosslingual STS. In Proceedings of SemEval 2016, pages 663–667.

Islam Beltagy, Stephen Roller, Pengxiang Cheng, Katrin Erk, and Raymond J Mooney. 2016. Representing meaning with a combination of logical and distributional models. Computational Linguistics. John R Firth. 1957. A synopsis of linguistic theory, 1930-1955. Blackwell.

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Aistats, volume 9, pages 249–256.

Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. 2016. A representation learning framework for multi-source transfer parsing. In Proc. of AAAI.

Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Omer Levy and Yoav Goldberg. 2014. Dependencybased word embeddings. In ACL, pages 302–308.

Chi-kiu Lo, Cyril Goutte, and Michel Simard. 2016. Cnrc at semeval-2016 task 1: Experiments in crosslingual semantic textual similarity. Proceedings of SemEval, pages 668–673.

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Robert Östling and Jörg Tiedemann. 2016. Efficient word alignment with markov chain monte carlo. The Prague Bulletin of Mathematical Linguistics, 106(1):125–146.

Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014.

Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958.

Citeringar i Crossref