238 citations · 1.1k across the 27 of their papers we have counts for
27 papers
Latent Template Induction with Gumbel-CRFs
Yao Fu, Chuanqi Tan, Bin Bi +3
Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structur…
Exploring Question-Specific Rewards for Generating Deep Questions
Yuxi Xie, Liangming Pan, Dongzhe Wang +2
Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing…
Neighborhood Matching Network for Entity Alignment
Yuting Wu, Xiao Liu, Yansong Feng +2
Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignmen…
Semantic Graphs for Generating Deep Questions
Liangming Pan, Yuxi Xie, Yansong Feng +2
This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input…
Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Kun Xu, Linfeng Song, Yansong Feng +2
Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local…
Paraphrase Generation with Latent Bag of Words
Yao Fu, Yansong Feng, John P. Cunningham
Paraphrase generation is a longstanding important problem in natural language processing. In addition, recent progress in deep generative models has shown promising results on disc…
Integrating Relation Constraints with Neural Relation Extractors
Yuan Ye, Yansong Feng, Bingfeng Luo +2
Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks NNs. However, such models often make predictions for each en…
Jointly Learning Entity and Relation Representations for Entity Alignment
Yuting Wu, Xiao Liu, Yansong Feng +2
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs). Recent developments in the field often take an embedding-based ap…
A Sketch-Based System for Semantic Parsing
Zechang Li, Yuxuan Lai, Yuxi Xie +2
This paper presents our semantic parsing system for the evaluation task of open domain semantic parsing in NLPCC 2019. Many previous works formulate semantic parsing as a sequence-…
Generating Classical Chinese Poems from Vernacular Chinese
Zhichao Yang, Pengshan Cai, Yansong Feng +4
Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of genera…
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Yuting Wu, Xiao Liu, Yansong Feng +3
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based metho…
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
Jiazhan Feng, Chongyang Tao, Wei Wu +3
We study learning of a matching model for response selection in retrieval-based dialogue systems. The problem is equally important with designing the architecture of a model, but i…
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Kun Xu, Liwei Wang, Mo Yu +4
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities…
Lattice CNNs for Matching Based Chinese Question Answering
Yuxuan Lai, Yansong Feng, Xiaohan Yu +3
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages lik…
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Liwei Chen, Yansong Feng, Songfang Huang +2
Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, questio…
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
Qing Qin, Jie Ren, Jialong Yu +6
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-constrained…
Overview of CAIL2018: Legal Judgment Prediction Competition
Haoxi Zhong, Chaojun Xiao, Zhipeng Guo +8
In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to pred…
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection
Chongyang Tao, Wei Wu, Can Xu +3
In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots. Existing models, su…
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction
Chaojun Xiao, Haoxi Zhong, Zhipeng Guo +8
In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \da…
Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
Bingfeng Luo, Yansong Feng, Zheng Wang +3
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper…
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Kun Xu, Lingfei Wu, Zhiguo Wang +3
The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have in…
Scale Up Event Extraction Learning via Automatic Training Data Generation
Ying Zeng, Yansong Feng, Rong Ma +4
The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training…
Learning to Predict Charges for Criminal Cases with Legal Basis
Bingfeng Luo, Yansong Feng, Jianbo Xu +2
The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue tha…
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
Bingfeng Luo, Yansong Feng, Zheng Wang +4
Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the gener…