36 citations · 154 across the 17 of their papers we have counts for
16 papers · 1 filter
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
Zebin Ou, Meishan Zhang, Yue Zhang
Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language…
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
Xin Zhang, Guangwei Xu, Yueheng Sun +2
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focu…
End-to-end Semantic Role Labeling with Neural Transition-based Model
Hao Fei, Meishan Zhang, Bobo Li +1
End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Re…
Cross-lingual Semantic Role Labeling with Model Transfer
Hao Fei, Meishan Zhang, Fei Li +1
Prior studies show that cross-lingual semantic role labeling (SRL) can be achieved by model transfer under the help of universal features. In this paper, we fill the gap of cross-l…
A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
Meishan Zhang
Syntactic and semantic parsing has been investigated for decades, which is one primary topic in the natural language processing community. This article aims for a brief survey on t…
DRTS Parsing with Structure-Aware Encoding and Decoding
Qiankun Fu, Yue Zhang, Jiangming Liu +1
Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a ne…
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Hao Fei, Meishan Zhang, Donghong Ji
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performance…
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge
Yijiang Liu, Meishan Zhang, Donghong Ji
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, sho…
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition
Jingye Li, Meishan Zhang, Donghong Ji +1
Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effe…
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank
Zhang Meishan, Zhang Yue, Fu Guohong
Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. The basic idea is to map dependency arcs from a source treebank to its targ…
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…
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…
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…
Combining Discrete and Neural Features for Sequence Labeling
Jie Yang, Zhiyang Teng, Meishan Zhang +1
Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural…
Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
Liner Yang, Meishan Zhang, Yang Liu +3
While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from t…
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
Fei Li, Meishan Zhang, Guohong Fu +2
Relation classification is associated with many potential applications in the artificial intelligence area. Recent approaches usually leverage neural networks based on structure fe…