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Summarizing Behaviours: An Experiment on the Annotation of Call-Centre Conversations

2016-05-01 · LREC 2016 5 · Morena Danieli, Balamurali A.R, Evgeny Stepanov, Benoit Favre, Frederic Bechet, Giuseppe Riccardi

Annotating and predicting behavioural aspects in conversations is becoming critical in the conversational analytics industry. In this paper we look into inter-annotator agreement of agent behaviour dimensions on two call center corpora. We find that the task can be annotated consistently over time, but that subjectivity issues impacts the quality of the annotation. The reformulation of some of the annotated dimensions is suggested in order to improve agreement.

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Assessment of Sales Negotiation Strategies with ISO 24617-2 Dialogue Act Annotations

2022-06-01 · ISA (LREC) 2022 6 · Jutta Stock, Volha Petukhova, Dietrich Klakow

Call centres endeavour to achieve the highest possible level of transparency with regard to the factors influencing sales success. Existing approaches to the quality assessment of customer-agent sales negotiations do not…

AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms

2023-02-20 · Dhaminda B. Abeywickrama, James Wilson, Suet Lee, Greg Chance 외

The behaviours of a swarm are not explicitly engineered. Instead, they are an emergent consequence of the interactions of individual agents with each other and their environment. This emergent functionality poses a chall…

Designing Deep Reinforcement Learning for Human Parameter Exploration

2019-07-01 · Hugo Scurto, Bavo Van Kerrebroeck, Baptiste Caramiaux, Frédéric Bevilacqua

Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this paper, we propose to investigate artific…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry

2025-12-18 · Chiara Di Vece, Zhehua Mao, Netanell Avisdris, Brian Dromey 외 arxiv

Accurate fetal growth assessment from ultrasound (US) relies on precise biometry measured by manually identifying anatomical landmarks in standard planes. Manual landmarking is time-consuming, operator-dependent, and sen…

Domain Adaptation

On the Geometric Ergodicity of Hamiltonian Monte Carlo

2016-01-29 · Samuel Livingstone, Michael Betancourt, Simon Byrne, Mark Girolami

We establish general conditions under which Markov chains produced by the Hamiltonian Monte Carlo method will and will not be geometrically ergodic. We consider implementations with both position-independent and position…

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