activity
20152020
most citedA Structured Self-attentive Sentence Embedding

1.5k citations · 1.9k across the 17 of their papers we have counts for

collaborators

27 papers

eess.AS2020

Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis

Guanghui Xu, Wei Song, Zhengchen Zhang +3

Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which ma…

cs.CL2020

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training

Yingyao Wang, Junwei Bao, Guangyi Liu +4

This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep…

cs.CL202014 cited

Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product

Tiangang Zhu, Yue Wang, Haoran Li +3

Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the a…

cs.CL2019

Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification

Xiaochen Hou, Jing Huang, Guangtao Wang +2

Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence. Most current approaches mainly consider the semantic inf…

cs.CL2019

Zero-shot Text-to-SQL Learning with Auxiliary Task

Shuaichen Chang, Pengfei Liu, Yun Tang +3

Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task. However, little work has paid attention to how these models generalize to realisti…

cs.LG201955 cited

Multiple instance learning with graph neural networks

Ming Tu, Jing Huang, Xiaodong He +1

Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN)…