17 citations · 70 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
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…