Thanks to our host : Xebia
• Guillaume Lample, FAIR [Talk in English]
Unsupervised machine translation
Machine translation (MT) has achieved impressive results recently, thanks to recent advances in deep learning and the availability of large-scale parallel corpora. Yet, their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs.
Previous studies have shown that monolingual data — widely available in most languages — can be used to improve the performance of MT systems. However, these were used to augment, rather than replace, parallel corpora.
In this talk, I will present our recent research on Unsupervised Machine Translation, where we show that it is possible to train MT systems in a fully unsupervised setting, without the need of any cross-lingual dictionary or parallel resources whatsoever, but with access only to large monolingual corpora in each language. Beyond translating languages for which there is no parallel data, our method could potentially be used to decipher unknown languages.
• Thomas Wolf, Hugging Face [Talk in English]
Neural networks based dialog agents: going beyond the seq2seq model
I will present a summary of the technical tools behind our submission to the Conversational Intelligence Challenge 2 which is part of NIPS 2018 (convai.io).
This challenge tests how a dialog agent can incorporate personality as well as common sense reasoning in a free-form setting.
Our submission is leading the leaderboard topping all tested metrics with a significant margin over the second top model.
These strong improvements are obtained by an innovative use of transfert learning, data augmentation technics and multi-task learning in a non-seq2seq architecture.