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Found 9 papers, 6 papers with code
An Analysis of State-of-the-Art Models for Situated Interactive MultiModal Conversations (SIMMC)
There is a growing interest in virtual assistants with multimodal capabilities, e. g., inferring the context of a conversation through scene understanding.
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.
Continual Learning in Task-Oriented Dialogue Systems
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
Situated and Interactive Multimodal Conversations
Next generation virtual assistants are envisioned to handle multimodal inputs (e. g., vision, memories of previous interactions, in addition to the user's utterances), and perform multimodal actions (e. g., displaying a route in addition to generating the system's utterance).
Single Channel Speech Enhancement Using Outlier Detection
A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed.
