Papers with noisy dataset
A Computational Analysis of Vagueness in Revisions of Instructional Texts (2021.eacl-srw)
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| Challenge: | We analyze edits that involve cases of vagueness in instructional texts . we extract and analyze version pairs of an instruction before and after a revision . |
| Approach: | They propose to extract and analyze edits that involve cases of vagueness in instructions . they adopt a pairwise ranking task to show improvements over existing baselines . |
| Outcome: | The proposed model can distinguish between two versions of an instruction in a noisy dataset. |
Where do LLMs currently stand on biomedical NER in both clean and noisy settings ? (2026.findings-eacl)
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| Challenge: | despite advances in medicine, many diseases remain without effective treatments . clinical meta-analysis is essential for drug discovery and clinical research . |
| Approach: | They investigate the performance of large language models (LLMs) on biomedical NER tasks . findings suggest LLMs exhibit a notable degree of robustness to noise . |
| Outcome: | The proposed models are closing the performance gap with BERT-based models and demonstrate particular strengths in low-data settings. |
A Meaning-Based Statistical English Math Word Problem Solver (N18-1)
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| Challenge: | Experimental results show that the proposed approach understands the meaning of each quantity in the text more. |
| Approach: | They propose a meaning-based approach for solving English math word problems . they analyze text, transform body and question parts into corresponding logic forms . Statistical models are proposed to select operator and operands . |
| Outcome: | The proposed approach outperforms existing systems on benchmark and noisy datasets. |
Classifying Sluice Occurrences in Dialogue (L18-1)
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| Challenge: | Ellipsis is an important challenge for natural language processing systems, says a new paper . previous work on ellipsis focused on news data, but sluicing presents a challenge for dialogue systems . |
| Approach: | They describe a corpus of 4100 sluice occurrences from the NYTimes Gigaword corpus . they build a classifier model to automatically classify slujce . |
| Outcome: | The proposed corpus contains 4100 sluice occurrences, with an accuracy of 67% . the work will support empirical research into slujcing in dialogue systems . |
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (N19-1)
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| Challenge: | Character-level models of tokens are effective at dealing with within-token noise and out-of-vocabulary words. |
| Approach: | They propose to eliminate the need for tokenizers by using a character-level semi-Markov conditional random field that uses neural networks for its character and segment representations. |
| Outcome: | The proposed model outperforms state-of-the-art part-of speech taggers on a noisy English dataset. |
ICC : Quantifying Image Caption Concreteness for Multimodal Dataset Curation (2024.findings-acl)
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| Challenge: | Existing methods to curation text-image data are noisy and lack the fine-grained ability to isolate the most concrete samples that provide the strongest signal for learning in a noisy dataset. |
| Approach: | They propose a metric that evaluates caption text without an image reference to measure its concreteness and relevancy. |
| Outcome: | The proposed method detects the concreteness of captions without an image reference and correlates with human evaluation of concreteness in both single-word and caption-level texts. |