Conversational AI

This paper proposes a simple phone mapping approach to multi-dialect acoustic modeling. In contrast to the widely used shared hidden layer (SHL) training approach (hidden layers are shared across dialects whereas output layers are kept separate), phone mapping simplifies model training and maintenance by allowing all the network parameters to be shared; it also simplifies online adaptation via HMM-based

End-to-end neural models for goal-oriented conversational systems have become an increasingly active area of research, though results in real-world settings are few. We present real-world results for two issue types in the customer service domain. We train models on historical chat transcripts and test on live contacts using a human-in-the-loop research platform. Additionally, we incorporate customer profile

Relation Extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intrasentence RE and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address

Sentiment analysis is a task that may suffer from a lack of data in certain cases, as the datasets are often generated and annotated by humans. In cases where data is inadequate for training discriminative models, generate models may aid training via data augmentation. Generative Adversarial Networks (GANs) are one such model that has advanced the state of the art in several tasks, including as image and

We present a novel approach to dialogue state tracking and referring expression resolution tasks. Successful contextual understanding of multi-turn spoken dialogues requires resolving referring expressions across turns and tracking the entities relevant to the conversation across turns. Tracking conversational state is particularly challenging in a multi-domain scenario when there exist multiple spoken