Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

The field of natural language processing has seen impressive progress in recent years, with
neural network models replacing many of the traditional systems. A plethora of new models …

Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …

With the growing popularity of deep-learning based NLP models, comes a need for
interpretable systems. But what is interpretability, and what constitutes a high-quality …

Attention mechanisms have seen wide adoption in neural NLP models. In addition to
improving predictive performance, these are often touted as affording transparency: models …

Attention mechanisms have recently boosted performance on a range of NLP tasks.
Because attention layers explicitly weight input components' representations, it is also often …

Attention mechanisms have become a popular component in deep neural networks, yet
there has been little examination of how different influencing factors and methods for …

The great success of Transformer-based models benefits from the powerful multi-head self-
attention mechanism, which learns token dependencies and encodes contextual information …

The attention layer in a neural network model provides insights into the model's reasoning
behind its prediction, which are usually criticized for being opaque. Recently, seemingly …