Large pre-trained neural networks such as BERT have had great recent success in NLP,
motivating a growing body of research investigating what aspects of language they are able …

Generating explanations for neural networks has become crucial for their applications in real-
world with respect to reliability and trustworthiness. In natural language processing, existing …

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

Pre-trained language models have demonstrated impressive performance in both natural
language processing and program understanding, which represent the input as a token …

Natural language processing (NLP) has been one of the subfields of artificial intelligence
much affected by the recent neural revolution. Architectures such as recurrent neural …

The state-of-the-art systems for most natural language engineering tasks employ machine
learning methods. Despite the improved performances of these systems, there is a lack of …

Named entity recognition (NER) is an important task in natural language processing (NLP).
Until the revival of neural network based models for NLP, NER taggers employed traditional …

Explainability in machine learning remains a critical unsolved challenge that slows the
adoption of machine learning systems in real-world applications. Machine learning …