Intent Detection
Intent detection is a natural-language-understanding task that classifies the purpose behind a user utterance — such as making a reservation, asking for information, or filing a complaint — into one of a set of predefined intent classes. It is a core NLU component of conversational interfaces and customer-service automation systems, drawing on the benchmarks of Larson et al. (2019) and Casanueva et al. (2020).
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Larson, S. et al. (2019). An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. EMNLP. · DOI 10.18653/v1/D19-1131
- Casanueva, I. et al. (2020). Efficient Intent Detection with Dual Sentence Encoders. ACL Workshop on NLP for Conversational AI. · DOI 10.18653/v1/2020.nlp4convai-1.5
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.