Papers with noisy texts
Learning Mutually Informed Representations for Characters and Subwords (2024.findings-naacl)
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| Challenge: | Pretrained language models rely on subword tokenization to process text as a sequence of subwords. |
| Approach: | They propose a character-subword language model that integrates character and subword modalities into one model. |
| Outcome: | The proposed model outperforms its backbone language models on English sequence labeling and classification tasks. |
Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks (2024.findings-naacl)
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| Challenge: | Offensive language detection is important for filtering out abusive expressions, authors argue . authors propose user-intended adversarial attacks that insert special symbols or leverage distinctive features of the Korean language. |
| Approach: | They propose user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. |
| Outcome: | The proposed models are more robust to performance degradation even when the attack rate is increased, compared to models trained on noisy texts. |
Visual Cues and Error Correction for Translation Robustness (2021.findings-emnlp)
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| Challenge: | Existing robustness techniques fail when faced with unseen types of noise and their performance degrades on clean texts. |
| Approach: | They propose visual context to improve translation robustness for noisy texts . they also propose an error correction training regime that can be used as an auxiliary task . |
| Outcome: | The proposed training regime improves translation robustness on noisy texts while maintaining translation quality on clean texts. |
From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (2020.acl-main)
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| Challenge: | Existing approaches to disambiguate entity mentions in a text depend on training data. |
| Approach: | They propose a domain-agnostic approach to annotate entities using a KB-based approach. |
| Outcome: | The proposed approach outperforms existing methods in a simulation on difficult texts. |
Unsupervised Named Entity Disambiguation for Low Resource Domains (2024.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Disambiguation (NED) are inefficient for domain specific tasks such as searching, question answering and information extraction. |
| Approach: | They propose a unsupervised approach leveraging the concept of Group Steiner Trees which can identify the most relevant candidate for entity disambiguation using contextual similarities across candidate entities for all the mentions present in a document. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods by more than 40% in terms of Precision@1 and Hit@5 across various domain-specific datasets. |