416 citations · 2.2k across the 58 of their papers we have counts for
7 papers · 1 filter
Compressing Neural Language Models by Sparse Word Representations
Yunchuan Chen, Lili Mou, Yan Xu +2
Neural networks are among the state-of-the-art techniques for language modeling. Existing neural language models typically map discrete words to distributed, dense vector represent…
Improved Knowledge Base Completion by Path-Augmented TransR Model
Wenhao Huang, Ge Li, Zhi Jin
Knowledge base completion aims to infer new relations from existing information. In this paper, we propose path-augmented TransR (PTransR) model to improve the accuracy of link pre…
Searching Action Proposals via Spatial Actionness Estimation and Temporal Path Inference and Tracking
Nannan Li, Dan Xu, Zhenqiang Ying +2
In this paper, we address the problem of searching action proposals in unconstrained video clips. Our approach starts from actionness estimation on frame-level bounding boxes, and…
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Lili Mou, Yiping Song, Rui Yan +3
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactor…
How Transferable are Neural Networks in NLP Applications?
Lili Mou, Zhao Meng, Rui Yan +4
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which…
Backward and Forward Language Modeling for Constrained Sentence Generation
Lili Mou, Rui Yan, Ge Li +2
Recent language models, especially those based on recurrent neural networks (RNNs), make it possible to generate natural language from a learned probability. Language generation ha…