Search Results for author:
Found 16 papers, 8 papers with code
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems
Open-domain dialog systems aim to generate relevant, informative and engaging responses.
Multi-Sentence Knowledge Selection in Open-Domain Dialogue
Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research.
Entity Resolution in Open-domain Conversations
In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest.
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access Track in DSTC9
This challenge track aims to expand the coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Example-Driven Intent Prediction with Observers
Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances.
DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue
A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains.
Ranked #5 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access
In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems
In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc.
Just Ask:An Interactive Learning Framework for Vision and Language Navigation
We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines
To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset.
Ranked #16 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Key-Value Retrieval Networks for Task-Oriented Dialogue
Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base.
Ranked #8 on
Task-Oriented Dialogue Systems
on KVRET
The Pragmatics of Indirect Commands in Collaborative Discourse
We focus on a less understood family of utterances for eliciting agent action, locatives like \emph{The chair is in the other room}, and demonstrate how these utterances indirectly command in specific game state contexts.
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
SceneSeer: 3D Scene Design with Natural Language
We present SceneSeer: an interactive text to 3D scene generation system that allows a user to design 3D scenes using natural language.
A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics.
