Conversational Repair Strategies for Customer Service Chatbot Errors

Data, statistics, and survey for the publication 'Conversational Repair Strategies to Cope with Errors and Breakdowns in Customer Service Chatbot Conversations'. The dataset is based on the thesis by Jasmin Verhagen and was contributed by author Anouck Braggaar to DataverseNL.

Use Cases

• Analyze user and chatbot repair strategies based on the described conversational data.
• Study the frequency and types of errors and breakdowns in customer service chatbot conversations.
• Evaluate the effectiveness of different conversational repair techniques mentioned in the associated publication.
• Train or benchmark NLP models for error detection and recovery in task-oriented dialogue systems.

Strengths

• Dataset is directly linked to a peer-reviewed publication with a DOI (10.1007/978-3-031-54975-5_2).
• Includes multiple data components: raw data, statistics, and a survey, as mentioned in the description.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.
• Data may reflect the specific scope and biases inherent to the original thesis research.

Provenance

Source

Based on the thesis by Jasmin Verhagen (https://arno.uvt.nl/show.cgi?fid=162648).

Collection Method

Likely contains conversational logs, derived statistics, and survey responses related to chatbot interactions.

Freshness

Last updated 2026-06-22 06:10:21; freshness should be verified.