most citedTraining Neural Response Selection for Task-Oriented Dialogue Systems

55 citations · 64 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2020

Multidirectional Associative Optimization of Function-Specific Word Representations

Daniela Gerz, Ivan Vulić, Marek Rei +2

We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joi…

cs.CL2020

Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations

Sam Coope, Tyler Farghly, Daniela Gerz +2

We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task. This formulation allows for a simple integration…

cs.CL2020

Efficient Intent Detection with Dual Sentence Encoders

Iñigo Casanueva, Tadas Temčinas, Daniela Gerz +2

Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivate…

cs.CL2019

PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking

Matthew Henderson, Ivan Vulić, Iñigo Casanueva +7

We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of t…

cs.CL201955 cited

Training Neural Response Selection for Task-Oriented Dialogue Systems

Matthew Henderson, Ivan Vulić, Daniela Gerz +7

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application bei…

cs.CL20199 cited

A Repository of Conversational Datasets

Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva +8

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a r…