45 citations · 153 across the 14 of their papers we have counts for
6 papers · 1 filter
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes
Xinyuan Zhang, Ruiyi Zhang, Manzil Zaheer +1
High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the f…
Learning Compressed Sentence Representations for On-Device Text Processing
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman +5
Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations a…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Dinghan Shen, Xinyuan Zhang, Ricardo Henao +1
Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of…
Joint Embedding of Words and Labels for Text Classification
Guoyin Wang, Chunyuan Li, Wenlin Wang +5
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to vie…
Diffusion Maps for Textual Network Embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen +1
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices. Rather than using typical natu…