Papers with adversarial learning framework
Adversarial Scrubbing of Demographic Information for Text Classification (2021.emnlp-main)
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Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, Snigdha Chaturvedi
| Challenge: | Existing frameworks to debias contextual representations can encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated task. |
| Approach: | They propose an adversarial learning framework to debias contextual representations by encoding undesirable attributes while being trained for an unrelated task. |
| Outcome: | The proposed framework debiases representations on 8 datasets while remaining informative on the target task. |
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (P18-1)
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| Challenge: | Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. |
| Approach: | They propose a sentence-level true-positive generator to learn a true-negative generator from a fuzzy sentence bag. |
| Outcome: | The proposed method significantly improves the performance of distant supervision relation extraction compared to state-of-the-art systems. |
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER (2021.acl-long)
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| Challenge: | Named entity recognition models rely on expensive labeled data for training, which is not always available across languages. |
| Approach: | They propose an adversarial approach where an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarially trained discriminators. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on standard benchmark datasets and outperformed existing methods on the target language. |
KBGAN: Adversarial Learning for Knowledge Graph Embeddings (N18-1)
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| Challenge: | Existing knowledge graph embedding techniques lack the capability to access similarities between entities and relations. |
| Approach: | They propose an adversarial learning framework to improve knowledge graph embedding models . they use one knowledge graph embedded model as a negative sample generator . |
| Outcome: | The proposed framework improves the performance of knowledge graph embedding models on a link prediction task. |
Counterfactual Off-Policy Training for Neural Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing models for open-domain dialogue generation suffer from data insufficiency . a potential response inferred in hindsight is called a counterfactual reasoning . |
| Approach: | They propose to explore potential responses by counterfactual reasoning . given an observed response, the model automatically infers the outcome of an alternative policy that could have been taken . |
| Outcome: | The proposed model outperforms the HRED model and conventional learning frameworks on the DailyDialog dataset. |
Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts (P19-1)
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| Challenge: | Existing why-QA methods retrieve “answer passages” that consist of several sentences . AGR is a vector representation of the non-redundant reason sought by a why-question . |
| Approach: | They propose a method for why-question answering that uses an adversarial learning framework. |
| Outcome: | The proposed method improves state-of-the-art open-domain QA on Japanese datasets . it also improves a state- of-the art method on publicly available English datasets. |
Enhancing LLM-Based Social Bot via an Adversarial Learning Framework (2025.emnlp-main)
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| Challenge: | Social media platforms provide an ideal testbed for large language models that exhibit human-like behavior. |
| Approach: | They propose an LLM-based social **Bot that enhances human-like generative capabilities through an adversarial learning framework. |
| Outcome: | The proposed framework generates human-like content aligned with diverse user profiles . it exhibits strong social responsiveness, more accurately modeling opinion dynamics . |