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Found 23 papers, 11 papers with code
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.
AnyTOD: A Programmable Task-Oriented Dialog System
We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training.
Speech Aware Dialog System Technology Challenge (DSTC11)
The research on this topic is stymied by the lack of a public corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.
A Unified Approach to Entity-Centric Context Tracking in Social Conversations
Second, we open-source a neural network architecture for context tracking.
Description-Driven Task-Oriented Dialog Modeling
Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks.
SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems
Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research.
UIBert: Learning Generic Multimodal Representations for UI Understanding
Our key intuition is that the heterogeneous features in a UI are self-aligned, i. e., the image and text features of UI components, are predictive of each other.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines
We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work.
Text-to-Text Pre-Training for Data-to-Text Tasks
We study the pre-train + fine-tune strategy for data-to-text tasks.
Ranked #1 on
Data-to-Text Generation
on ToTTo
Template Guided Text Generation for Task-Oriented Dialogue
Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language.
Ranked #2 on
Data-to-Text Generation
on MULTIWOZ 2.1
Schema-Guided Dialogue State Tracking Task at DSTC8
The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks
Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation.
Learning Question-Guided Video Representation for Multi-Turn Video Question Answering
Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans.
Multi-task learning for Joint Language Understanding and Dialogue State Tracking
This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems.
An Efficient Approach to Encoding Context for Spoken Language Understanding
In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames.
Spoken Language Understanding
Task-Oriented Dialogue Systems
Building a Conversational Agent Overnight with Dialogue Self-Play
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains.
Scalable Multi-Domain Dialogue State Tracking
We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
A Fast Unified Model for Parsing and Sentence Understanding
Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences.
Ranked #87 on
Natural Language Inference
on SNLI
