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20182022
most citedSpin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6

10 citations · 58 across the 8 of their papers we have counts for

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8 papers · 1 filter

cs.CL20206 cited

Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

Pengda Qin, Xin Wang, Wenhu Chen +3

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion…

cs.CL20196 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.CL20196 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.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.CL201910 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…

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…