activity
20152022
most citedDiscourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers

151 citations · 266 across the 27 of their papers we have counts for

collaborators

46 papers

cs.CL20221 cited

A Survey for Efficient Open Domain Question Answering

Qin Zhang, Shangsi Chen, Dongkuan Xu +4

Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language pr…

cs.CL20221 cited

Incorporating Constituent Syntax for Coreference Resolution

Fan Jiang, Trevor Cohn

Syntax has been shown to benefit Coreference Resolution from incorporating long-range dependencies and structured information captured by syntax trees, either in traditional statis…

cs.CL2021

It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data

Jinming Zhao, Philip Arthur, Gholamreza Haffari +2

Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on r…

cs.CL2021

Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data

Kemal Kurniawan, Lea Frermann, Philip Schulz +1

Providing technologies to communities or domains where training data is scarce or protected e.g., for privacy reasons, is becoming increasingly important. To that end, we generalis…

cs.CL202119 cited

Contrastive Learning for Fair Representations

Aili Shen, Xudong Han, Trevor Cohn +2

Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing me…

cs.CL2021

Fairness-aware Class Imbalanced Learning

Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin +2

Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the…