Summary of A New Approach For Fine-tuning Sentence Transformers For Intent Classification and Out-of-scope Detection Tasks, by Tianyi Zhang et al.
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks
by Tianyi Zhang, Atta Norouzian, Aanchan Mohan, Frederick Ducatelle
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach to rejecting user queries that fall outside the scope of virtual assistant systems. By combining out-of-scope rejection with intent classification on in-scope queries, the method uses transformer-based sentence encoders to produce high-quality embeddings. However, recent work has shown that fine-tuning these encoders for intent-classification tasks can disperse in-scope embeddings over the full sentence embedding space, making it difficult to reject out-of-sample instances. To address this issue, the authors introduce a regularization term that learns an auto-encoder to reconstruct in-scope embeddings, which improves the accuracy of out-of-scope rejection without compromising intent classification performance. The method achieves a 1-4% improvement in the area under the precision-recall curve for rejecting OOS instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps virtual assistant systems get better at handling user queries that don’t make sense or are outside what they can handle. It combines two important tasks: figuring out what users mean (intent classification) and deciding when to reject their requests if they’re too hard to understand. The problem is that current methods for doing this spread out the good information (in-scope embeddings) so much that it gets mixed up with bad information (out-of-scope embeddings). To fix this, the authors came up with a new way to train the model using an auto-encoder that helps keep the good information contained. This makes it easier for the system to correctly reject requests that are outside its scope. |
Keywords
» Artificial intelligence » Classification » Embedding space » Encoder » Fine tuning » Precision » Recall » Regularization » Transformer
