activity
20162023
most citedUsing millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

431 citations · 857 across the 69 of their papers we have counts for

collaborators
Showing 2020Show all

9 papers · 1 filter

cs.CL2020

Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses

Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis +2

Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum…

cs.CL2020

Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards

Rahul Aralikatte, Mostafa Abdou, Heather Lent +2

Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, an…

cs.CL2020

Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias

Ana Valeria Gonzalez, Maria Barrett, Rasmus Hvingelby +2

The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highl…

cs.CL2020★ 9 cited

Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfer

Sheng Zhang, Xin Zhang, Weiming Zhang +1

Multi-task transfer learning based on pre-trained language encoders achieves state-of-the-art performance across a range of tasks. Standard approaches implicitly assume the tasks,…

cs.CL2020

The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

Mostafa Abdou, Vinit Ravishankar, Maria Barrett +3

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sens…

cs.CL2020

We Need to Talk About Random Splits

Anders Søgaard, Sebastian Ebert, Jasmijn Bastings +1

Gorman and Bedrick (2019) argued for using random splits rather than standard splits in NLP experiments. We argue that random splits, like standard splits, lead to overly optimisti…