285 citations · 1.2k across the 36 of their papers we have counts for
14 papers · 1 filter
EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models
Zhengwei Tao, Zhi Jin, Xiaoying Bai +4
Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language proce…
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
Bolin Wei, Shuai Lu, Lili Mou +4
This paper addresses the question: Why do neural dialog systems generate short and meaningless replies? We conjecture that, in a dialog system, an utterance may have multiple equal…
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Zhao Meng, Lili Mou, Zhi Jin
Neural network-based dialog systems are attracting increasing attention in both academia and industry. Recently, researchers have begun to realize the importance of speaker modelin…
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Zhao Meng, Lili Mou, Zhi Jin
Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is us…
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…
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…
Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation
Yan Xu, Ran Jia, Lili Mou +4
Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a…
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…
A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
Hao Peng, Lili Mou, Ge Li +3
This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely s…
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
Xu Yan, Lili Mou, Ge Li +3
Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the…
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…