Summary of Improved Out-of-scope Intent Classification with Dual Encoding and Threshold-based Re-classification, by Hossam M. Zawbaa et al.
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification
by Hossam M. Zawbaa, Wael Rashwan, Sourav Dutta, Haytham Assem
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Dual Encoder for Threshold-Based Re-Classification (DETER) is an end-to-end framework designed to detect out-of-scope user utterances in task-oriented dialogues and intent classification. By utilizing dual text encoders, Universal Sentence Encoder (USE) and Transformer-based Denoising AutoEncoder (TSDAE), DETER generates user utterance embeddings that are classified through a branched neural architecture. The approach also includes self-supervision to generate synthetic outliers and incorporate out-of-scope phrases from open-domain datasets. A threshold-based re-classification mechanism refines the model’s initial predictions. Evaluations on CLINC-150, Stackoverflow, and Banking77 datasets show DETER’s efficacy, outperforming previous benchmarks by up to 13% and 5% in F1 score for known and unknown intents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DETER is a new way to detect when someone is talking about something that isn’t part of the conversation. This happens often in chatbots or virtual assistants. The old ways of doing this didn’t work very well because they made too many assumptions about how the data would look. DETER is different because it can handle unexpected data without needing extra steps after processing. It uses two special kinds of computers to understand what people are saying and then decides if their statement fits with the conversation or not. This approach works better than previous methods, making it more accurate in detecting out-of-scope statements. |
Keywords
» Artificial intelligence » Autoencoder » Classification » Encoder » F1 score » Transformer
