3 citations · 4 across the 2 of their papers we have counts for
6 papers
Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation
Jason Lee, Raphael Shu, Kyunghyun Cho
We propose an efficient inference procedure for non-autoregressive machine translation that iteratively refines translation purely in the continuous space. Given a continuous laten…
On the Discrepancy between Density Estimation and Sequence Generation
Jason Lee, Dustin Tran, Orhan Firat +1
Many sequence-to-sequence generation tasks, including machine translation and text-to-speech, can be posed as estimating the density of the output y given the input x: p(y|x). Give…
Countering Language Drift via Visual Grounding
Jason Lee, Kyunghyun Cho, Douwe Kiela
Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans. We find that agents that were initially pretrained to…
Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior
Raphael Shu, Jason Lee, Hideki Nakayama +1
Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latenc…
Multi-Turn Beam Search for Neural Dialogue Modeling
Ilia Kulikov, Jason Lee, Kyunghyun Cho
In neural dialogue modeling, a neural network is trained to predict the next utterance, and at inference time, an approximate decoding algorithm is used to generate next utterances…
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Jason Lee, Elman Mansimov, Kyunghyun Cho
We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models an…