Intent Detection
Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.
Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
Papers
Showing 1–10 of 330 papers
All datasetsATISMixSNIPSMixATISSNIPSASOS.com user intentBANKING77ATIS (vi)BANKING77 10-shotBANKING77 5-shotCAISCLINC150CLINC150 10-shot
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | DGIF | Accuracy | 83.3 | — | Unverified |
| 2 | UGEN | Accuracy | 83 | — | Unverified |
| 3 | TFMN (PACL) | Accuracy | 82.9 | — | Unverified |
| 4 | SLIM (PACL) | Accuracy | 81.9 | — | Unverified |
| 5 | BiSLU | Accuracy | 81.5 | — | Unverified |
| 6 | TFMN | Accuracy | 79.8 | — | Unverified |
| 7 | RoBERTa (PACL) | Accuracy | 79.1 | — | Unverified |
| 8 | Co-guiding Net | Accuracy | 79.1 | — | Unverified |
| 9 | Uni-MIS | Accuracy | 78.5 | — | Unverified |
| 10 | SLIM | Accuracy | 78.3 | — | Unverified |