most citedExtract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation

4 citations · 6 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL20194 cited

Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation

Jiawei Wu, Xin Wang, William Yang Wang

The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs. Back-translation has been domina…

cs.CV2019

VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

Xin Wang, Jiawei Wu, Junkun Chen +3

We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions,…

cs.CL20192 cited

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

Wenhan Xiong, Jiawei Wu, Deren Lei +4

Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such…

cs.CL2018

Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning

Xin Wang, Jiawei Wu, Da Zhang +2

Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to…

cs.CL2018

Reinforced Co-Training

Jiawei Wu, Lei Li, William Yang Wang

Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted la…