activity
20192022
most citedOn the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation

8 citations · 15 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

8 papers · 1 filter

cs.CL2022

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack

Yu Cao, Dianqi Li, Meng Fang +4

We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while main…

cs.CL20228 cited

On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation

Changtong Zan, Liang Ding, Li Shen +3

Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains (someti…

cs.CL2022

Interpretable Proof Generation via Iterative Backward Reasoning

Hanhao Qu, Yu Cao, Jun Gao +2

We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series o…

cs.CL20221 cited

A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation

Yu Cao, Wei Bi, Meng Fang +2

Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue…

cs.CL20216 cited

DAGN: Discourse-Aware Graph Network for Logical Reasoning

Yinya Huang, Meng Fang, Yu Cao +2

Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting amo…

cs.CL2021

Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation

Yu Cao, Liang Ding, Zhiliang Tian +1

Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore…