E. Agirre, C. Banea, C. Cardie, D. Cer, M. Diab et al., Semeval-2014 task 10: Multilingual semantic textual similarity, Proceedings of the 8th International Workshop on Semantic Evaluation, pp.81-91, 2014.
DOI : 10.3115/v1/s14-2010

URL : https://doi.org/10.3115/v1/s14-2010

J. Auguste, A. Rey, and B. Favre, Evaluation of word embeddings against cognitive processes: Primed reaction times in lexical decision and naming tasks, Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, pp.21-26, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01773220

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching Word Vectors with Subword Information, Transactions of the Association of Computational Linguistics, vol.5, issue.1, pp.135-146, 2017.
DOI : 10.1162/tacl_a_00051

URL : https://doi.org/10.1162/tacl_a_00051

E. Bruni, G. Boleda, M. Baroni, and N. K. Tran, Distributional semantics in technicolor, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol.1, p.120, 2012.

A. Conneau and D. Kiela, SentEval: An Evaluation Toolkit for Universal Sentence Representations, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018.

A. Conneau, D. Kiela, H. Schwenk, L. Barrault, and A. Bordes, Supervised Learning of Universal Sentence Representations from Natural Language Inference Data, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp.670-680, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01897968

A. Conneau, G. Kruszewski, G. Lample, L. Barrault, and M. Baroni, What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.2126-2136, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01898412

F. Hill, K. Cho, and A. Korhonen, Learning Distributed Representations of Sentences from Unlabelled Data, Proceedings of NAACL-HLT, pp.1367-1377, 2016.
DOI : 10.18653/v1/n16-1162

URL : https://doi.org/10.18653/v1/n16-1162

F. Hill, R. Reichart, and A. Korhonen, Simlex-999: Evaluating semantic models with (genuine) similarity estimation, Computational Linguistics, p.173, 2016.
DOI : 10.1162/coli_a_00237

URL : http://arxiv.org/pdf/1408.3456

R. Kiros, Y. Zhu, R. R. Salakhutdinov, R. Zemel, R. Urtasun et al., Skipthought vectors, Advances in neural information processing systems, pp.3294-3302, 2015.

Q. Le and T. Mikolov, Distributed representations of sentences and documents, International Conference on Machine Learning, pp.1188-1196, 2014.

T. Luong, R. Socher, and C. D. Manning, Better word representations with recursive neural networks for morphology, CoNLL, p.192, 2013.

M. Marelli, S. Menini, M. Baroni, L. Bentivogli, R. Bernardi et al., A SICK cure for the evaluation of compositional distributional semantic models, LREC, pp.216-223, 2014.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, Proceedings of Workshop at ICLR, p.3267, 2013.

J. Pennington, R. Socher, and C. D. Manning, Glove: Global Vectors for Word Representation, EMNLP, vol.14, p.1307, 2014.

R. Perrotin, A. Nasr, and J. Auguste, Dialog Acts Annotations for Online Chats, 25e Conférence Sur Le Traitement Automatique Des Langues Naturelles (TALN), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01943345

L. Pragst, N. Rach, W. Minker, and S. Ultes, On the Vector Representation of Utterances in Dialogue Context, p.LREC, 2018.

A. Søgaard, Evaluating word embeddings with fMRI and eye-tracking, ACL, p.0, 2016.