In this work, we examine the problems associated with neural dialog models under the common theme of compositionality.
The advent and fast development of neural networks have revolutionized the research on dialogue systems and subsequently have triggered various challenges regarding their automatic evaluation.
Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness.
To address these issues, we devise a cascaded modular system leveraging self-supervised discrete speech units as language representation.
The development of Open-Domain Dialogue Systems (ODS)is a trending topic due to the large number of research challenges, large societal and business impact, and advances in the underlying technology.
In this paper, we present a novel architecture to realize fine-grained style control on the transformer-based text-to-speech synthesis (TransformerTTS).
While Wav2Vec 2. 0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies.
Our system grounds language on the level of edit operations, and suggests options for a user to choose from.
2 code implementations • 31 Jan 2019 • Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W. black, Alexander Rudnicky, Jason Williams, Joelle Pineau, Mikhail Burtsev, Jason Weston
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
RubyStar is a dialog system designed to create "human-like" conversation by combining different response generation strategies.
Since the classifier is meant to serve as a module in a practical dialog system, it needs to be able to work with limited training data and incorporate new data on the fly.