activity
20192025
most citedOption Comparison Network for Multiple-choice Reading Comprehension

50 citations · 101 across the 10 of their papers we have counts for

collaborators
Showing cs.CLShow all

11 papers · 1 filter

cs.CL2022

A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models

Deming Ye, Yankai Lin, Peng Li +2

Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose…

cs.CL20211 cited

Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction

Tianyu Gao, Xu Han, Keyue Qiu +7

Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data. However, its evaluation has l…

cs.CL2020

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

Xu Han, Tianyu Gao, Yankai Lin +7

Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on rel…

cs.CL20191 cited

DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog

Feilong Chen, Fandong Meng, Jiaming Xu +3

Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image. It remains a challenging task since it requires the…

cs.CL2019

Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information

Qiu Ran, Yankai Lin, Peng Li +1

Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration. However, existing NAT models still hav…

cs.CL20195 cited

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification

Tianyu Gao, Xu Han, Hao Zhu +4

We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of inst…