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Evaluating cross-lingual textual similarity on dictionary alignment problem


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.

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  • Alonge, A., Bertagna, F., Calzolari, N., Roventini, A. (1999). The italian wordnet. Deliverable D032D033, EuroWordNet.

  • Alvarez-Melis, D., Jaakkola, T. (2018). Gromov-Wasserstein Alignment of Word Embedding Spaces. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 1881–1890.

  • Arora, S., Liang, Y., Ma, T. (2016). A Simple but Tough-to-Beat Baseline for Sentence Embeddings.

  • Artetxe, M., Labaka, G., Agirre, E. (2018a). A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 789–798.

  • Artetxe, M., Labaka, G., Agirre, E. (2018b). A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. arXiv preprint arXiv:180506297.

  • Balikas, G., Laclau, C., Redko, I., Amini, M.R. (2018). Cross-lingual Document Retrieval using Regularized Wasserstein Distance. In: Proceedings of the 40th European Conference ECIR conference on Information Retrieval, ECIR 2018, Grenoble, France, March 26-29, 2018.

  • Balkova, V., Sukhonogov, A., Yablonsky, S. (2004). Russian wordnet. In: Proceedings of the Second Global Wordnet Conference.

  • Barrón-Cedeño, A., Rosso, P., Agirre, E., Labaka, G. (2010). Plagiarism Detection Across Distant Language Pairs. In: Proceedings of the 23rd International Conference on Computational Linguistics, Association for Computational Linguistics, Stroudsburg, PA, USA, COLING ’10, pp 37–45,, event-place: Beijing, China.

  • Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155.

    Google Scholar 

  • Bojanowski, P., Grave, E., Joulin, A., Mikolov, T. (2016). Enriching word vectors with subword information. arXiv preprint arXiv:160704606.

  • Bond, F., & Paik, K. (2012). A Survey of WordNets and their Licenses. GWC 2012 6th International Global Wordnet Conference, 8, 64.

    Google Scholar 

  • Cuturi, M. (2013). Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances. arXiv:13060895 [stat] 1306.0895.

  • Diab, M. (2004). The Feasibility of Bootstrapping an Arabic Wordnet Leveraging Parallel Corpora and an English Wordnet. In: Proceedings of the Arabic Language Technologies and Resources, NEMLAR, Cairo.

  • Ercan, G., & Haziyev, F. (2019). Synset expansion on translation graph for automatic wordnet construction. Information Processing & Management, 56(1), 130–150.

    Article  Google Scholar 

  • Fellbaum, C. (1998). WordNet: An Electronic Lexical Database. Language, Speech, and Communication. New York: MIT Press.

    Book  Google Scholar 

  • Fišer, D., Novak, J., Erjavec, T. (2012). sloWNet 3.0: Development, extension and cleaning. In: Proceedings of 6th International Global Wordnet Conference (GWC 2012), pp. 113–117.

  • Franco-Salvador, M., Rosso, P., & Montes-y Gómez, M. (2016). A systematic study of knowledge graph analysis for cross-language plagiarism detection. Information Processing & Management, 52(4), 550–570.

    Article  Google Scholar 

  • Glavas, G., Litschko, R., Ruder, S., Vulic, I. (2019). How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions. arXiv:190200508 [cs] 1902.00508.

  • Glorot, X., Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp 249–256.

  • Gouws, S., Bengio, Y., Corrado, G. (2015). Bilbowa: Fast bilingual distributed representations without word alignments. In: Proceedings of the 32nd International Conference on Machine Learning,

  • Grigoriadou, M., Kornilakis, H., Galiotou, E., Stamou, S., & Papakitsos, E. (2004). The software infrastructure for the development and validation of the Greek WordNet. Romanian Journal of Information Science and Technology, 7(1–2), 89–105.

    Google Scholar 

  • Hamp, B., Feldweg, H. (1997). Germanet-a lexical-semantic net for german. In: Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications.

  • Jawanpuria, P., Balgovind, A., Kunchukuttan, A., & Mishra, B. (2019). Learning multilingual word embeddings in latent metric space: A geometric approach. Transactions of the Association for Computational Linguistics, 7, 107–120.

    Article  Google Scholar 

  • Johnson, A., Karanasou, P., Gaspers, J., Klakow, D. (2019). Cross-lingual transfer learning for japanese named entity recognition. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pp. 182–189.

  • Khodak, M., Risteski, A., Fellbaum, C., Arora, S. (2017). Automated WordNet Construction Using Word Embeddings. In: Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pp. 12–23.

  • Klementiev, A., Titov, I., & Bhattarai, B. (2012). Inducing crosslingual distributed representations of words. Proceedings of COLING, 2012, 1459–1474.

    Google Scholar 

  • Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q. (2015). From Word Embeddings to Document Distances. In: Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37,, ICML’15, pp. 957–966.

  • Lam, K.N., Tarouti, F.A., Kalita, J. (2014). Automatically constructing Wordnet Synsets. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 106–111,

  • Leng, Y., Tan, X., Qin, T., Li, X.Y., Liu, T.Y. (2019). Unsupervised Pivot Translation for Distant Languages. arXiv:190602461 [cs] 1906.02461.

  • Lison, P., Tiedemann, J. (2016). Opensubtitles2016: Extracting large parallel corpora from movie and tv subtitles. International Conference on Language Resources and Evaluation.

  • Litschko, R., Glavaš, G., Ponzetto, S.P., Vulić, I. (2018). Unsupervised cross-lingual information retrieval using monolingual data only. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM, pp 1253–1256.

  • Luong, T., Pham, H., Manning, C.D. (2015). Bilingual word representations with monolingual quality in mind. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp. 151–159.

  • Mikolov, T., Le, Q.V., Sutskever, I. (2013a). Exploiting Similarities among Languages for Machine Translation. arXiv:13094168 [cs] 1309.4168.

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J. (2013b). Distributed Representations of Words and Phrases and their Compositionality. arXiv:13104546 [cs, stat], arXiv: 1310.4546.

  • Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., Joulin, A. (2018). Advances in pre-training distributed word representations. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018).

  • Mogadala, A., Rettinger, A. (2016). Bilingual word embeddings from parallel and non-parallel corpora for cross-language text classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 692–702.

  • Pascanu, R., Mikolov, T., Bengio, Y. (2012). On the difficulty of training Recurrent Neural Networks. arXiv:12115063 [cs] 1211.5063.

  • Pedersen, B. S., Nimb, S., Asmussen, J., Sørensen, N. H., Trap-Jensen, L., & Lorentzen, H. (2009). DanNet: The challenge of compiling a wordnet for Danish by reusing a monolingual dictionary. Language Resources and Evaluation, 43(3), 269–299.

    Article  Google Scholar 

  • Pennington, J., Socher, R., Manning, C. (2014). Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532–1543.

  • Potthast, M., Barrón-Cedeño, A., Stein, B., & Rosso, P. (2011). Cross-language plagiarism detection. Language Resources and Evaluation, 45(1), 45–62.

    Article  Google Scholar 

  • Rubner, Y., Tomasi, C., Guibas, L.J. (1998). A metric for distributions with applications to image databases. In: Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp. 59–66

  • Ruci, E. (2008). On the current state of Albanet and related applications. Tech. rep., Technical report, University of Vlora.(http://fjalnet. com...).

  • Ruder, S., Vulić, I., & Søgaard, A. (2019). A survey of cross-lingual word embedding models. Journal of Artificial Intelligence Research,.

  • Ruiz-Casado, M., Alfonseca, E., Castells, P. (2005). Automatic assignment of wikipedia encyclopedic entries to wordnet synsets. In: International Atlantic Web Intelligence Conference, Springer, pp 380–386.

  • Rupnik, J., Muhic, A., Leban, G., Skraba, P., Fortuna, B., & Grobelnik, M. (2016). News across languages-cross-lingual document similarity and event tracking. Journal of Artificial Intelligence Research, 55, 283–316.

    Article  Google Scholar 

  • Sagot, B., Fišer, D. (2008). Building a free French wordnet from multilingual resources. In: OntoLex.

  • Sand, H., Velldal, E., Øvrelid, L. (2017). Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet. In: Proceedings of the 21st Nordic Conference on Computational Linguistics, pp. 298–302.

  • Simov, K.I., Osenova, P. (2010). Constructing of an Ontology-based Lexicon for Bulgarian. In: LREC, Citeseer.

  • Sinkhorn, R., & Knopp, P. (1967). Concerning nonnegative matrices and doubly stochastic matrices. Pacific Journal of Mathematics, 21(2), 343–348.

    Article  Google Scholar 

  • Stamou, S., Nenadic, G., Christodoulakis, D. (2004). Exploring Balkanet Shared Ontology for Multilingual Conceptual Indexing. In: LREC

  • Taghizadeh, N., & Faili, H. (2016). Automatic wordnet development for low-resource languages using cross-lingual wsd. Journal of Artificial Intelligence Research, 56, 61–87.

    Article  Google Scholar 

  • Toral, A., Bracale, S., Monachini, M., Soria, C. (2010). Rejuvenating the Italian WordNet: Upgrading, standardising, extending. In: Proceedings of the 5th International Conference of the Global WordNet Association (GWC-2010), Mumbai

  • Tufiş, D., Barbu, E., Mititelu, V. B., Ion, R., & Bozianu, L. (2004). The romanian wordnet. Romanian Journal on Information Science and Technology, 7(2–3), 105–122.

    Google Scholar 

  • Tufiş, D., Ion, R., Bozianu, L., Ceauşu, A., Ştefănescu, D. (2008). Romanian wordnet: Current state, new applications and prospects. In: Proceedings of 4th Global WordNet Conference, GWC, pp. 441–452.

  • Upadhyay, S., Faruqui, M., Dyer, C., Roth, D. (2016). Cross-lingual Models of Word Embeddings: An Empirical Comparison. arXiv:160400425 [cs] 1604.00425.

  • Vossen, P. (1998). Introduction to EuroWordNet. Computers and the Humanities, 32(2/3), 73–89.

    Article  Google Scholar 

  • Vulić, I., Moens, M.F. (2015). Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp. 363–372.

  • Zeiler, M.D. (2012). ADADELTA: An Adaptive Learning Rate Method. arXiv:12125701 [cs] 1212.5701.

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This study was supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK), with award no. 114E776.

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Correspondence to Gönenç Ercan.

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Sever, Y., Ercan, G. Evaluating cross-lingual textual similarity on dictionary alignment problem. Lang Resources & Evaluation 54, 1059–1078 (2020).

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  • Cross-lingual textual semantic similarity
  • Word embeddings
  • Wasserstein distance
  • Sinkhorn distance
  • Siamese neural network