| Challenge: | Recent years have seen the rise of community question answering forums . duplicate questions easily become ubiquitous as users often ask the same question, possibly in a slightly different formulation, making it difficult to find the best (or one correct) answer. |
| Approach: | They propose to use domain adaptation to detect duplicate questions in forums . they find that domain adaptation improves performance over multiple pairs of domains . |
| Outcome: | The proposed approach improves 5.6% over the best baseline across multiple pairs of domains. |
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| Challenge: | Recent studies have used alternative methods to train neural models to duplicate question detection in community Question Answering forums. |
| Approach: | They propose two new methods for supervised question detection in community Question Answering forums . they propose weak supervision using title and body of question and automatic generation of duplicate questions . |
| Outcome: | The proposed methods can achieve better performance even without labeled data. |
Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection (D19-1)
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| Challenge: | Stack Exchange has fewer than 160 user-labeled duplicates, and 25% have fewer. |
| Approach: | They propose a framework that combines sentence encoders with unlabeled data to solve the problem of duplicate question detection in Community Question Answering forums. |
| Outcome: | The proposed framework outperforms BM25, a single-view system and a supervised domain-adversarial DQD method on the CQADupStack corpus and on Stack Exchange forums. |
Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks (P19-1)
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| Challenge: | Existing domain adaptation techniques for question deduplication and RTE focus on transferring category independent knowledge between domains. |
| Approach: | They propose to use gradient reversal to explicitly learn shared and unshared (domain specific) representations between two textual domains to compensate for domain mismatch while distilling domain specific knowledge. |
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Adversarial Domain Adaptation for Machine Reading Comprehension (D19-1)
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| Challenge: | Existing models for machine reading comprehension rely on large amounts of human-annotated in-domain data. |
| Approach: | They propose an unsupervised domain adaptation framework for Machine Reading Comprehension where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. |
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Defending Against Disinformation Attacks in Open-Domain Question Answering (2024.eacl-short)
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| Challenge: | Existing methods to defend against data poisoning attacks in open-domain question answering are lacking . |
| Approach: | They propose a method that uses query augmentation to find diverse passages that could answer the original question but are less likely to have been poisoned. |
| Outcome: | The proposed method provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts. |
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. |
Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection (2025.findings-acl)
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| Challenge: | Existing models for fake news detection capture domain-shared semantic features but fail to generalize well due to poor adaptability. |
| Approach: | They propose a framework to enable structure knowledge transfer between multiple domains . they compare content-only and propagation-rich data to preserve structural patterns . |
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Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (P18-1)
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| Challenge: | Statistical parsers are often criticized for their performance outside of the domain they were trained on . we show that word representations reduce the need for domain adaptation when the target domain is syntactically similar to the source domain. |
| Approach: | They propose a way to adapt a parser to a syntactically similar target domain using partial annotations. |
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Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters (2021.findings-acl)
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| Challenge: | Existing approaches to ED are limited due to complexity of textual data and domain shift problem. |
| Approach: | They propose to use domain adapter-based Adaptation framework to improve event detection across domains. |
| Outcome: | The proposed framework significantly boosts the performance on target domains. |
Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation (2020.acl-main)
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| Challenge: | supervised event trigger identification models can generalize better across domains . prior work focused on annotating specific categories of events or narratives from specific domains. |
| Approach: | They propose to use adversarial domain adaptation framework to build supervised event trigger identification models which can generalize better across domains. |
| Outcome: | The proposed model improves on literature and news domains with no labeled data. |