We have long envisioned that one day computers will understand natural language and
anticipate what we need, when and where we need it, and proactively complete tasks on our …

Spoken language understanding and dialog management have emerged as key
technologies in interacting with personal digital assistants (PDAs). The coverage …

Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term
Memory (LSTM), have been widely used for sequence labeling. In this paper, we first …

To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is
important to predict users' intents behind their utterances and their satisfaction with the …

We present a novel approach to dialogue state tracking and referring expression resolution
tasks. Successful contextual understanding of multi-turn spoken dialogues requires …

Artificial Intelligence (AI) systems are increasingly pervasive: Internet of Things, in-car
intelligent devices, robots, and virtual assistants, and their large-scale adoption makes it …

Intent detection is one of the main tasks of a dialogue system. In this paper, we present our
intent detection system that is based on fastText word embeddings and a neural network …

In human-computer conversation systems, the context of a user-issued utterance is
particularly important because it provides useful background information of the conversation …

Accurate intent detection-based chatbots are usually trained on larger datasets that are not
available for some languages. Seeking the most accurate models, three English benchmark …

Multi-party Conversational Systems are systems with natural language interaction between
one or more people or systems. From the moment that an utterance is sent to a group, to the …