Rim Helaoui


2022

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Towards In-Context Non-Expert Evaluation of Reflection Generation for Counselling Conversations
Zixiu Wu | Simone Balloccu | Rim Helaoui | Diego Reforgiato Recupero | Daniele Riboni
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Reflection is an essential counselling strategy, where the therapist listens actively and responds with their own interpretation of the client’s words. Recent work leveraged pre-trained language models (PLMs) to approach reflection generation as a promising tool to aid counsellor training. However, those studies used limited dialogue context for modelling and simplistic error analysis for human evaluation. In this work, we take the first step towards addressing those limitations. First, we fine-tune PLMs on longer dialogue contexts for reflection generation. Then, we collect free-text error descriptions from non-experts about generated reflections, identify common patterns among them, and accordingly establish discrete error categories using thematic analysis. Based on this scheme, we plan for future work a mass non-expert error annotation phase for generated reflections followed by an expert-based validation phase, namely “whether a coherent and consistent response is a good reflection”.

2021

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Towards Low-Resource Real-Time Assessment of Empathy in Counselling
Zixiu Wu | Rim Helaoui | Diego Reforgiato Recupero | Daniele Riboni
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Gauging therapist empathy in counselling is an important component of understanding counselling quality. While session-level empathy assessment based on machine learning has been investigated extensively, it relies on relatively large amounts of well-annotated dialogue data, and real-time evaluation has been overlooked in the past. In this paper, we focus on the task of low-resource utterance-level binary empathy assessment. We train deep learning models on heuristically constructed empathy vs. non-empathy contrast in general conversations, and apply the models directly to therapeutic dialogues, assuming correlation between empathy manifested in those two domains. We show that such training yields poor performance in general, probe its causes, and examine the actual effect of learning from empathy contrast in general conversation.

2018

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Interactive health insight miner: an adaptive, semantic-based approach
Isabel Funke | Rim Helaoui | Aki Härmä
Proceedings of the 11th International Conference on Natural Language Generation

E-health applications aim to support the user in adopting healthy habits. An important feature is to provide insights into the user’s lifestyle. To actively engage the user in the insight mining process, we propose an ontology-based framework with a Controlled Natural Language interface, which enables the user to ask for specific insights and to customize personal information.