Papers with adversarial discriminator
Deconfounded Lexicon Induction for Interpretable Social Science (N18-1)
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| Challenge: | Lexical features are useful beyond predictive performance. they can also be used to understand the subjective properties of a text. |
| Approach: | They propose two deep learning algorithms that separate the explanatory power of text from confounds. |
| Outcome: | The proposed algorithms are predictive of a set of target variables yet uncorrelated to confounds . they pick words associated with narrative persuasion and are more predictive than standard features . |
Evaluating Rewards for Question Generation Models (N19-1)
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| Challenge: | Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. |
| Approach: | They propose to use a Seq2Seq architecture to train models to generate one-step-ahead predictions, but at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process. |
| Outcome: | The proposed model is trained to generate a plausible question, conditioned on an input document and answer span within that document. |
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. |