activity
20162023
most citedDeep Reinforcement Learning for Visual Object Tracking in Videos

101 citations · 489 across the 33 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

eess.AS2019★ 9 cited

Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

Erica Cooper, Cheng-I Lai, Yusuke Yasuda +4

While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zer…

cs.CL2019★ 6 cited

Self-Supervised Dialogue Learning

Jiawei Wu, Xin Wang, William Yang Wang

The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations. We conside…

cs.CL2019★ 6 cited

Self-Supervised Learning for Contextualized Extractive Summarization

Hong Wang, Xin Wang, Wenhan Xiong +4

Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.…

cs.CL2019★ 4 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★ 39 cited

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.CL2019★ 10 cited

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models

Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang +4

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several…