238 citations · 1.1k across the 27 of their papers we have counts for
23 papers · 1 filter
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…
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…
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…
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…
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification
Yaoyuan Zhang, Zhenxu Ye, Yansong Feng +2
Sentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved…
Question Answering on Freebase via Relation Extraction and Textual Evidence
Kun Xu, Siva Reddy, Yansong Feng +2
Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they ar…
Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Kun Xu, Yansong Feng, Songfang Huang +1
Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subject…