Abstract Natural Language Processing (NLP) is one of the most captivating applications of
Deep Learning. In this survey, we consider how the Data Augmentation training strategy can …

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

In tasks like semantic parsing, instruction following, and question answering, standard deep
networks fail to generalize compositionally from small datasets. Many existing approaches …

Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …

Counterfactual explanations are a prominent example of post-hoc interpretability methods in
the explainable Artificial Intelligence (AI) research domain. Differently from other explanation …

We propose a novel data-augmentation technique for neural machine translation based on
ROT-$ k $ ciphertexts. ROT-$ k $ is a simple letter substitution cipher that replaces a letter in …

Conversational recommender systems~(CRSs) aim to provide recommendation services via
natural language conversations. Although a number of approaches have been proposed for …

The construction of open-domain dialogue systems requires high-quality dialogue datasets.
The dialogue data admits a wide variety of responses for a given dialogue history, especially …

The task of context-dependent text-to-SQL aims to convert multi-turn user utterances to
formal SQL queries. This is a challenging task due to both the scarcity of training data from …