29 citations · 44 across the 6 of their papers we have counts for
8 papers · 1 filter
Reward Optimization for Neural Machine Translation with Learned Metrics
Raphael Shu, Kang Min Yoo, Jung-Woo Ha
Neural machine translation (NMT) models are conventionally trained with token-level negative log-likelihood (NLL), which does not guarantee that the generated translations will be…
GraphPlan: Story Generation by Planning with Event Graph
Hong Chen, Raphael Shu, Hiroya Takamura +1
Story generation is a task that aims to automatically produce multiple sentences to make up a meaningful story. This task is challenging because it requires high-level understandin…
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…
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…
Real-time Neural-based Input Method
Jiali Yao, Raphael Shu, Xinjian Li +2
The input method is an essential service on every mobile and desktop devices that provides text suggestions. It converts sequential keyboard inputs to the characters in its target…
Discrete Structural Planning for Neural Machine Translation
Raphael Shu, Hideki Nakayama
Structural planning is important for producing long sentences, which is a missing part in current language generation models. In this work, we add a planning phase in neural machin…