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        <title>HumaniCA: A Benchmark Resource for the Detection of Users’ Ascription of Humanness to Conversational Agents</title>
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    <abstract>Anthropomorphizing, which involves attributing human-like characteristics to non-human entities, is common in users’ conversations with text-based conversational agents and can lead to a misalignment between the users’ expectations and the agent’s actual capabilities. Detecting users’ ascriptions of humanness automatically may enable systems to identify when users adopt a human-like style when conversing with an agent and to adapt its responses accordingly to tune their expectations. In this paper, we introduce HumaniCA, a benchmark resource comprising three annotated datasets of user turns from real dialogues with three different types of conversational agents (task-oriented, Q&amp;A, and LLM-based) aimed at indicating whether the user is ascribing humanness to the conversational agent. We also identified a set of linguistic indicators of user ascription of humanness to conversational agents and validated their utility with benchmark experiments. We then compared performance of our linguistic features and other well-known textual features (TF-IDF weights and SentenceBERT word embeddings), as well as their combinations. The evaluation highlights the central role of our linguistic features: whether used individually or in combination, they consistently achieve higher accuracy across all agent types.</abstract>
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%0 Conference Proceedings
%T HumaniCA: A Benchmark Resource for the Detection of Users’ Ascription of Humanness to Conversational Agents
%A Villata, Sabrina
%A Rapp, Amon
%A Di Caro, Luigi
%A Cena, Federica
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F villata-etal-2026-humanica
%X Anthropomorphizing, which involves attributing human-like characteristics to non-human entities, is common in users’ conversations with text-based conversational agents and can lead to a misalignment between the users’ expectations and the agent’s actual capabilities. Detecting users’ ascriptions of humanness automatically may enable systems to identify when users adopt a human-like style when conversing with an agent and to adapt its responses accordingly to tune their expectations. In this paper, we introduce HumaniCA, a benchmark resource comprising three annotated datasets of user turns from real dialogues with three different types of conversational agents (task-oriented, Q&A, and LLM-based) aimed at indicating whether the user is ascribing humanness to the conversational agent. We also identified a set of linguistic indicators of user ascription of humanness to conversational agents and validated their utility with benchmark experiments. We then compared performance of our linguistic features and other well-known textual features (TF-IDF weights and SentenceBERT word embeddings), as well as their combinations. The evaluation highlights the central role of our linguistic features: whether used individually or in combination, they consistently achieve higher accuracy across all agent types.
%R 10.63317/3uujwof4yj3o
%U https://aclanthology.org/2026.lrec-1.221/
%U https://doi.org/10.63317/3uujwof4yj3o
%P 2825-2835

Markdown (Informal)

[HumaniCA: A Benchmark Resource for the Detection of Users’ Ascription of Humanness to Conversational Agents](https://aclanthology.org/2026.lrec-1.221/) (Villata et al., LREC 2026)

• HumaniCA: A Benchmark Resource for the Detection of Users’ Ascription of Humanness to Conversational Agents (Villata et al., LREC 2026)

ACL

• Sabrina Villata, Amon Rapp, Luigi Di Caro, and Federica Cena. 2026. HumaniCA: A Benchmark Resource for the Detection of Users’ Ascription of Humanness to Conversational Agents. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 2825–2835, Palma de Mallorca, Spain. ELRA Language Resource Association.