no code implementations • 24 May 2023 • Shufan Wang, Sebastien Jean, Sailik Sengupta, James Gung, Nikolaos Pappas, Yi Zhang
In executable task-oriented semantic parsing, the system aims to translate users' utterances in natural language to machine-interpretable programs (API calls) that can be executed according to pre-defined API specifications.
no code implementations • WS 2017 • Sebastien Jean, Stanislas Lauly, Orhan Firat, Kyunghyun Cho
In this paper we present our systems for the DiscoMT 2017 cross-lingual pronoun prediction shared task.
no code implementations • 17 Apr 2017 • Sebastien Jean, Stanislas Lauly, Orhan Firat, Kyunghyun Cho
We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence.
no code implementations • 19 Dec 2014 • Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio
Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model.
no code implementations • 2 Oct 2014 • Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio
Neural language models learn word representations that capture rich linguistic and conceptual information.