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20172021
most citedTraining Neural Response Selection for Task-Oriented Dialogue Systems

55 citations · 71 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CL20211 cited

ConvFiT: Conversational Fine-Tuning of Pretrained Language Models

Ivan Vulić, Pei-Hao Su, Sam Coope +5

Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence enco…

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.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…

cs.CL20176 cited

An Attention Mechanism for Answer Selection Using a Combined Global and Local View

Yoram Bachrach, Andrej Zukov-Gregoric, Sam Coope +4

We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input. Previous work focused on augmenting recurrent neural…