Schwenk, Holger

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 25,545

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 3,317

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 2,049

Very Deep Convolutional Networks for Text Classification

Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Topic ModelingText and Document Classification Technologies

Cited by 1,046

Seamless: Multilingual Expressive and Streaming Speech Translation

Communication, S., Barrault, L., Chung, Y.-A., Meglioli, M. C., Dale, D., Dong, N., Duppenthaler, M., Duquenne, P.-A., Ellis, B., Elsahar, H., et al. (2023) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 45