Papers with discriminative classifier
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)
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| Challenge: | Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection. |
| Approach: | They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training. |
| Outcome: | The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods. |
Controllable Contrastive Generation for Multilingual Biomedical Entity Linking (2023.emnlp-main)
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| Challenge: | Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in biomedically text to standardized concepts in a multilingual knowledge base (KB). |
| Approach: | They propose a prompt-based controllable contrastive generation framework for MBEL which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template. |
| Outcome: | The proposed framework matches against UMLS concepts in as many languages and types as possible, thus facilitating cross-information disambiguation. |
Uncertainty-aware generative models for inferring document class prevalence (D18-1)
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| Challenge: | Existing methods for inferring the relative frequency of classes of unlabeled examples are imperfect. |
| Approach: | They propose a generative probabilistic modeling approach to prevalence estimation . they back out an implicit individual-level likelihood function to conduct fast inference . |
| Outcome: | The proposed method provides better confidence interval coverage than an alternative and is significantly more robust to shifts in the class prior between training and testing. |