26 citations · 212 across the 74 of their papers we have counts for
13 papers · 2 filters
An Investigation of Potential Function Designs for Neural CRF
Zechuan Hu, Yong Jiang, Nguyen Bach +4
The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential function…
Second-Order Unsupervised Neural Dependency Parsing
Songlin Yang, Yong Jiang, Wenjuan Han +1
Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order sup…
Adversarial Attack and Defense of Structured Prediction Models
Wenjuan Han, Liwen Zhang, Yong Jiang +1
Building an effective adversarial attacker and elaborating on countermeasures for adversarial attacks for natural language processing (NLP) have attracted a lot of research in rece…
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Xinyu Wang, Yong Jiang, Nguyen Bach +4
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forwa…
A Survey of Unsupervised Dependency Parsing
Wenjuan Han, Yong Jiang, Hwee Tou Ng +1
Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no anno…
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
Xinyu Wang, Yong Jiang, Zhaohui Yan +6
Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The object…
Automated Concatenation of Embeddings for Structured Prediction
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concat…
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
Xinyu Wang, Kewei Tu
In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the…
More Embeddings, Better Sequence Labelers?
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings. However, there is no definite c…
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
Xinyu Wang, Yong Jiang, Kewei Tu
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}. Our system is a graph-based parser with second-order inference. For the low-resource Ta…
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Xinyu Wang, Yong Jiang, Nguyen Bach +3
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages. Compared with relying on multiple monolingual models, us…
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
Xinyu Wang, Yixian Liu, Zixia Jia +2
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}. Our system is a graph-based parser whi…
Learning Numeral Embeddings
Chengyue Jiang, Zhonglin Nian, Kaihao Guo +4
Word embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the pr…