43 citations · 144 across the 24 of their papers we have counts for
24 papers
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Tong Zhu, Xiaoye Qu, Wenliang Chen +4
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in b…
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Yu Zhang, Qingrong Xia, Shilin Zhou +3
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiqui…
Exploiting Rich Syntax for Better Knowledge Base Question Answering
Pengju Zhang, Yonghui Jia, Muhua Zhu +2
Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly fo…
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Jinpeng Zhang, Baijun Ji, Nini Xiao +4
Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word…
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
Juntao Li, Chang Liu, Chongyang Tao +4
Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context uttera…
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
Kun Wu, Lijie Wang, Zhenghua Li +5
Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness. The lack of labeled data for unseen evaluation…
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
Tong Zhu, Haitao Wang, Junjie Yu +4
In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-sc…
Token Drop mechanism for Neural Machine Translation
Huaao Zhang, Shigui Qiu, Xiangyu Duan +1
Neural machine translation with millions of parameters is vulnerable to unfamiliar inputs. We propose Token Drop to improve generalization and avoid overfitting for the NMT model.…
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
Xiangyu Duan, Baijun Ji, Hao Jia +5
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the abi…
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Yu Zhang, Zhenghua Li, Min Zhang
In the deep learning (DL) era, parsing models are extremely simplified with little hurt on performance, thanks to the remarkable capability of multi-layer BiLSTMs in context repres…
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
Houquan Zhou, Yu Zhang, Zhenghua Li +1
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing. But quite a few works focus on jo…
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation
Baijun Ji, Zhirui Zhang, Xiangyu Duan +3
Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods inv…
Improving Neural Relation Extraction with Positive and Unlabeled Learning
Zhengqiu He, Wenliang Chen, Yuyi Wang +3
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforceme…
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
Qingrong Xia, Zhenghua Li, Min Zhang
Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay mu…
Contrastive Attention Mechanism for Abstractive Sentence Summarization
Xiangyu Duan, Hoongfei Yu, Mingming Yin +3
We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a g…
Syntax-aware Neural Semantic Role Labeling
Qingrong Xia, Zhenghua Li, Min Zhang +4
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic…
Code-Switching for Enhancing NMT with Pre-Specified Translation
Kai Song, Yue Zhang, Heng Yu +3
Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags fo…
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Meishan Zhang, Zhenghua Li, Guohong Fu +1
Syntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained pars…
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
Wei Jiang, Zhenghua Li, Yu Zhang +1
This paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately…
Adversarial Learning for Chinese NER from Crowd Annotations
YaoSheng Yang, Meishan Zhang, Wenliang Chen +3
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be o…
Improved English to Russian Translation by Neural Suffix Prediction
Kai Song, Yue Zhang, Min Zhang +1
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when deali…
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
Zhengqiu He, Wenliang Chen, Zhenghua Li +3
Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from pla…
Training Dependency Parsers with Partial Annotation
Zhenghua Li, Yue Zhang, Jiayuan Chao +1
Recently, these has been a surge on studying how to obtain partially annotated data for model supervision. However, there still lacks a systematic study on how to train statistical…
Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
Qingrong Xia, Zhenghua Li, Jiayuan Chao +1
This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.