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2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.

Implicitly Abusive Language -- What does it actually look like and why are we not getting there?

technical paper

Multi-view Subword Regularization

Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

XOR QA: Cross-lingual Open-Retrieval Question Answering

Open Domain Question Answering over Tables via Dense Retrieval

Open-Domain Question Answering Goes Conversational via Question Rewriting

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Representing Numbers in NLP: a Survey and a Vision

https://d3smihljt9218e.cloudfront.net/transcript/13865/subtitle/2ed0e11cb40897ed52f5f828b7103265.vtt

English (automatic)

Implicitly Abusive Language -- What does it actually look like and why are we not getting there?

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Multi-view Subword Regularization

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