49 citations · 93 across the 7 of their papers we have counts for
9 papers
Scaling In-Context Demonstrations with Structured Attention
Tianle Cai, Kaixuan Huang, Jason D. Lee +1
The recent surge of large language models (LLMs) highlights their ability to perform in-context learning, i.e., "learning" to perform a task from a few demonstrations in the contex…
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…
Emergent Translation in Multi-Agent Communication
Jason Lee, Kyunghyun Cho, Jason Weston +1
While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting…
Fully Character-Level Neural Machine Translation without Explicit Segmentation
Jason Lee, Kyunghyun Cho, Thomas Hofmann
Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model t…