On Cross-Lingual Text Similarity Using Neural Translation Models

Author:

Seki Kazuhiro1

Affiliation:

1. Konan University

Publisher

Information Processing Society of Japan

Subject

General Computer Science

Reference31 articles.

1. [1] Arora, S., Liang, Y. and Ma, T.: A Simple but Tough-to-Beat Baseline for Sentence Embeddings, Proc. 5th International Conference on Learning Representations (2017).

2. [2] Bahdanau, D., Cho, K. and Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate, Proc. 3rd International Conference on Learning Representations (2015).

3. [3] Bengio, Y., Ducharme, R., Vincent, P. and Janvin, C.: A Neural Probabilistic Language Model, The Journal of Machine Learning Research, Vol.3, pp.1137-1155 (2003).

4. [4] Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I. and Specia, L.: SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation, Proc. 11th International Workshop on Semantic Evaluation (SemEval-2017), pp.1-14 (2017).

5. [5] Chandar A.P.S., Lauly, S., Larochelle, H., Khapra, M., Ravindran, B., Raykar, V.C. and Saha, A.: An Autoencoder Approach to Learning Bilingual Word Representations, Proc. 27th International Conference on Neural Information Processing Systems, pp.1853-1861 (2014).

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