Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Modern deep neural networks achieve impressive performance in engineering applications
that require extensive linguistic skills, such as machine translation. This success has …

Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained
Transformer-based Natural Language Understanding (NLU) models indicate that they …

This chapter critically examines the potential contributions of modern language models to
theoretical linguistics. Despite their focus on engineering goals, these models' ability to …

The prototypical NLP experiment trains a standard architecture on labeled English data and
optimizes for accuracy, without accounting for other dimensions such as fairness …

Dialect differences caused by regional, social, and economic factors cause performance
discrepancies for many groups of language technology users. Inclusive and equitable …

How do typological properties such as word order and morphological case marking affect
the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic …

The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop
BlackboxNLP was dedicated to resources and techniques specifically developed for …

Elazar and Goldberg (2018) showed that protected attributes can be extracted from the
representations of a debiased neural network for mention detection at above-chance levels …

Accurate syntactic representations are essential for robust generalization in natural
language. Recent work has found that pre-training can teach language models to rely on …