activity
20142022
most citedSequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

63 citations · 90 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD20222 cited

Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search

Lucas N. Ferreira, Lili Mou, Jim Whitehead +1

This paper presents a new approach for controlling emotion in symbolic music generation with Monte Carlo Tree Search. We use Monte Carlo Tree Search as a decoding mechanism to stee…

cs.CL2021

Search and Learn: Improving Semantic Coverage for Data-to-Text Generation

Shailza Jolly, Zi Xuan Zhang, Andreas Dengel +1

Data-to-text generation systems aim to generate text descriptions based on input data (often represented in the tabular form). A typical system uses huge training samples for learn…

cs.CL20161 cited

Dialogue Session Segmentation by Embedding-Enhanced TextTiling

Yiping Song, Lili Mou, Rui Yan +4

In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation. Howe…

cs.CL201611 cited

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…

cs.CL201663 cited

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…

cs.SE201413 cited

Building Program Vector Representations for Deep Learning

Lili Mou, Ge Li, Yuxuan Liu +4

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated feature…