In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

In recent years, reinforcement learning and bandits have transformed a wide range of real-
world applications including healthcare, finance, recommendation systems, robotics, and …

A computer-implemented method for dual sequence inference using a neural network model
includes generating a codependent representation based on a first input representation of a …

Traditional models for question answering optimize using cross entropy loss, which
encourages exact answers at the cost of penalizing nearby or overlapping answers that are …

Reinforcement Learning (RL) has been an interesting research area in Machine Learning
and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into …

Compositional generalization is a basic and essential intellective capability of human
beings, which allows us to recombine known parts readily. However, existing neural network …

We present and evaluate a novel approach to natural language generation (NLG) in
statistical spoken dialogue systems (SDS) using a data-driven statistical optimization …

Incremental processing allows system designers to address several discourse phenomena
that have previously been somewhat neglected in interactive systems, such as …

Natural Language Generation systems in interactive settings often face a multitude of
choices, given that the communicative effect of each utterance they generate depends …

Surface realisers in spoken dialogue systems need to be more responsive than
conventional surface realisers. They need to be sensitive to the utterance context as well as …