Domain Adaptation for Subjective Induction Questions Answering on Products by Adversarial Disentangled Learning (2024.acl-long)
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| Challenge: | Existing methods to answer subjective questions about products are often imbalanced across product domains. |
| Approach: | They propose a domain-adaptive model that integrates multiple viewpoints into a good answer by integrating these heterogeneous and inconsistent viewpoints. |
| Outcome: | The proposed model integrates multiple viewpoints into a single answer span and is able to integrate them into the answer. |
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| Challenge: | Adapting models to new domain without finetuning is a challenging problem in deep learning. |
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Adversarial Domain Adaptation for Duplicate Question Detection (D18-1)
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Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)
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| Challenge: | Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention . |
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| Challenge: | Existing studies have shown that adversarial data collection (ADC) models perform better on other adversarially collected data but are liable under plausible domain shifts. |
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