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20192022
most citedLarge-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs

8 citations · 20 across the 7 of their papers we have counts for

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cs.CL2022

FaiRR: Faithful and Robust Deductive Reasoning over Natural Language

Soumya Sanyal, Harman Singh, Xiang Ren

Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that suc…

cs.CL20211 cited

Commonsense-Focused Dialogues for Response Generation: An Empirical Study

Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia +5

Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and Commonsen…

cs.CL20214 cited

Discretized Integrated Gradients for Explaining Language Models

Soumya Sanyal, Xiang Ren

As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation. It…

cs.CL20211 cited

Improving Counterfactual Generation for Fair Hate Speech Detection

Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy +3

Bias mitigation approaches reduce models' dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features…

cs.CL20206 cited

Two Step Joint Model for Drug Drug Interaction Extraction

Siliang Tang, Qi Zhang, Tianpeng Zheng +7

When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interac…

cs.CL2020

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

Yankun Ren, Jianbin Lin, Siliang Tang +4

Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods ge…