Artificial neural network (ANN) is a simplified model of the biological nervous system
consisting of nerve cells or neurons. The application of ANN to food process engineering is …

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the
state of the conversation--such as the user's goal--given all of the dialog history up to that …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …

Natural language generation (NLG) is a critical component of spoken dialogue and it has a
significant impact both on usability and perceived quality. Most NLG systems in common use …

One of the core components of modern spoken dialogue systems is the belief tracker, which
estimates the user's goal at every step of the dialogue. However, most current approaches …

In this work, we present a novel counter-fitting method which injects antonymy and
synonymy constraints into vector space representations in order to improve the vectors' …

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the
preceding conversation. For multi-domain DST, the data sparsity problem is also a major …

Abstract We present Attract-Repel, an algorithm for improving the semantic quality of word
vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the …

In this work, we present a hybrid learning method for training task-oriented dialogue systems
through online user interactions. Popular methods for learning task-oriented dialogues …

Dialogue state tracker is the core part of a spoken dialogue system. It estimates the beliefs of
possible user's goals at every dialogue turn. However, for most current approaches, it's …