Papers with domain classifier
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding (N19-2)
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| Challenge: | Existing approaches to leveraging data across locales to improve domain classification accuracy are ineffective. |
| Approach: | They propose a locale-agnostic universal domain classification model that leverages available data across locales sharing the same language to improve domain classification accuracy. |
| Outcome: | The proposed model outperforms baseline models especially when classifying locale-specific domains and low-resourced domains. |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
| Outcome: | The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks. |
An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection (C18-1)
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| Challenge: | Existing approaches to spam detection focus on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. |
| Approach: | They propose a deep learning architecture for incorporating entities and their attributes into a unified framework. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two Yelp datasets. |
A Robust Information-Masking Approach for Domain Counterfactual Generation (2023.findings-acl)
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| Challenge: | Domain shift is a big challenge in NLP, but many approaches fail to leverage domain-specific nuances relevant to the task at hand. |
| Approach: | They propose a method that uses frequency-based masking to transform a text from the source domain to a target domain. |
| Outcome: | The proposed method outperforms baselines on 10 out of 12 domain-counterfactual classification settings with an average of 1.7% improvement in accuracy metric. |
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. |
| Outcome: | The proposed framework can be generalizable to different MRC models and datasets and can be extended to semi-supervised learning. |
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection (2021.emnlp-main)
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| Challenge: | Existing work on unsupervised domain adaptation of neural machine translation assumes access to monolingual text in either the source or target language in the new domain. |
| Approach: | They propose a method to extract in-domain sentences from a large generic monolingual corpus from 'missing' text. |
| Outcome: | The proposed method outperforms baselines up to +1.5 BLEU score on five diverse domains in three language pairs and a real-world translation scenario. |
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
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| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |
Improving Both Domain Robustness and Domain Adaptability in Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
| Approach: | They propose a meta-learning framework which improves domain robustness and adaptability . they use a word-level domain mixing model and a domain classifier to integrate it . |
| Outcome: | The proposed approach improves domain robustness and adaptability in seen and unseen domains. |
Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection (2022.coling-1)
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| Challenge: | Existing approaches for hate-speech detection exhibit poor performance in out-of-domain settings due to overemphasizing source-specific information that negatively impacts its domain invariance. |
| Approach: | They propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a classifier. |
| Outcome: | The proposed approach improves cross-domain evaluation on indomain held-out instances while preserving high performance on out-of-domain settings. |