Citation: Position Papers of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 22, pages 3–8 (2020)
Abstract. Natural language inference (NLI) is a sentence-pair classification task w.r.t. the entailment relation. As already shown, certain deep learning architectures for NLI task -- InferSent in particular -- may be exploited for obtaining (supervised) universal sentence embeddings. Although InferSent approach to sentence embeddings has been recently outperformed in different tasks by transformer-based architectures (like BERT and its derivatives), it still remains a useful tool in many NLP areas and it also serves as a strong baseline. One of the greatest advantages of this approach is its relative simplicity. Moreover, in contrast to other approaches, the training of InferSent models can be performed on a standard GPU within hours. Unfortunately, the majority of research on sentence embeddings in general is done in/for English, whereas other languages are apparently neglected. In order to fill this gab, we propose a methodology for obtaining universal sentence embeddings in another language -- arising from training InferSent-based sentence encoders on machine translated NLI corpus and present a transfer learning use-case on semantic textual similarity in Czech.
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