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20212026
most citedUsing Self-Supervised Feature Extractors with Attention for Automatic COVID-19 Detection from Speech

1 citations · 2 across the 5 of their papers we have counts for

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cs.CL2026

MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support

John Mendonça, Daniel Tschernutter, José Pombal +6

With people increasingly turning to AI chatbots for mental health support, the need to evaluate these tools is becoming more pressing. Recent benchmarks attempt to mimic a realisti…

cs.CL2024

Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs

John Mendonça, Isabel Trancoso, Alon Lavie

Although human evaluation remains the gold standard for open-domain dialogue evaluation, the growing popularity of automated evaluation using Large Language Models (LLMs) has also…

cs.CL2024

ECoh: Turn-level Coherence Evaluation for Multilingual Dialogues

John Mendonça, Isabel Trancoso, Alon Lavie

Despite being heralded as the new standard for dialogue evaluation, the closed-source nature of GPT-4 poses challenges for the community. Motivated by the need for lightweight, ope…

cs.CL2024★ 1 cited

On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation

John Mendonça, Alon Lavie, Isabel Trancoso

Large Language Models (LLMs) have showcased remarkable capabilities in various Natural Language Processing tasks. For automatic open-domain dialogue evaluation in particular, LLMs…

cs.CL2023

Towards Multilingual Automatic Dialogue Evaluation

John Mendonça, Alon Lavie, Isabel Trancoso

The main limiting factor in the development of robust multilingual dialogue evaluation metrics is the lack of multilingual data and the limited availability of open sourced multili…