activity
20192021
most citedStructure-Level Knowledge Distillation For Multilingual Sequence Labeling

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20211 cited

Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings

Xinyu Wang, Zixia Jia, Yong Jiang +1

This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task. Our system is a graph-based parser with the technique of Automated Concatena…

cs.CL2020

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…

cs.CL20203 cited

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…

cs.CL20201 cited

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…

cs.CL2019

Second-Order Semantic Dependency Parsing with End-to-End Neural Networks

Xinyu Wang, Jingxian Huang, Kewei Tu

Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency pars…