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20152021
most citedA Review of Evaluation Techniques for Social Dialogue Systems

9 citations · 23 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CL2021

A Study of Automatic Metrics for the Evaluation of Natural Language Explanations

Miruna Clinciu, Arash Eshghi, Helen Hastie

As transparency becomes key for robotics and AI, it will be necessary to evaluate the methods through which transparency is provided, including automatically generated natural lang…

cs.CL2020

The Lab vs The Crowd: An Investigation into Data Quality for Neural Dialogue Models

José Lopes, Francisco J. Chiyah Garcia, Helen Hastie

Challenges around collecting and processing quality data have hampered progress in data-driven dialogue models. Previous approaches are moving away from costly, resource-intensive…

cs.CL2020★ 8 cited

Transfer Learning for British Sign Language Modelling

Boris Mocialov, Graham Turner, Helen Hastie

Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning methods. This is partly due to greater computing power bu…

cs.CL2020

Towards Large-Scale Data Mining for Data-Driven Analysis of Sign Languages

Boris Mocialov, Graham Turner, Helen Hastie

Access to sign language data is far from adequate. We show that it is possible to collect the data from social networking services such as TikTok, Instagram, and YouTube by applyin…

cs.CL2018

Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots

Francisco J. Chiyah Garcia, David A. Robb, Xingkun Liu +3

Autonomous systems in remote locations have a high degree of autonomy and there is a need to explain what they are doing and why in order to increase transparency and maintain trus…

cs.CL2017★ 9 cited

A Review of Evaluation Techniques for Social Dialogue Systems

Amanda Cercas Curry, Helen Hastie, Verena Rieser

In contrast with goal-oriented dialogue, social dialogue has no clear measure of task success. Consequently, evaluation of these systems is notoriously hard. In this paper, we revi…