activity
20202022
most citedMulti-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network

2 citations · 6 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20222 cited

Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt

Xinyin Ma, Xinchao Wang, Gongfan Fang +2

Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the dependence of original training data, and has recently achieved impressive results in ac…

cs.CL2021

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Xinyin Ma, Yong Jiang, Nguyen Bach +4

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entit…

cs.CL2021

Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition

Yongliang Shen, Xinyin Ma, Zeqi Tan +3

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-b…

cs.CL20212 cited

A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction

Yongliang Shen, Xinyin Ma, Yechun Tang +1

Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency bet…

cs.CL2020

Adversarial Self-Supervised Data-Free Distillation for Text Classification

Xinyin Ma, Yongliang Shen, Gongfan Fang +3

Large pre-trained transformer-based language models have achieved impressive results on a wide range of NLP tasks. In the past few years, Knowledge Distillation(KD) has become a po…

cs.CL20202 cited

Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network

Zeyun Tang, Yongliang Shen, Xinyin Ma +3

Multi-hop reading comprehension across multiple documents attracts much attention recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehensio…