285 citations · 677 across the 10 of their papers we have counts for
10 papers
Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems
Kai Hua, Zhiyuan Feng, Chongyang Tao +2
Recently, knowledge-grounded conversations in the open domain gain great attention from researchers. Existing works on retrieval-based dialogue systems have paid tremendous efforts…
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…
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Lili Mou, Rui Men, Ge Li +4
In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) capt…
On End-to-End Program Generation from User Intention by Deep Neural Networks
Lili Mou, Rui Men, Ge Li +2
This paper envisions an end-to-end program generation scenario using recurrent neural networks (RNNs): Users can express their intention in natural language; an RNN then automatica…
Discriminative Neural Sentence Modeling by Tree-Based Convolution
Lili Mou, Hao Peng, Ge Li +3
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling. Our models leverage either constituency trees or dependency trees of sen…
Distilling Word Embeddings: An Encoding Approach
Lili Mou, Ran Jia, Yan Xu +3
Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are partic…
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…
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Lili Mou, Ge Li, Lu Zhang +2
Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the a…