Papers with WA
MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)
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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang
| Challenge: | Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations. |
| Approach: | They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features. |
| Outcome: | The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks. |
The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks (2025.findings-acl)
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| Challenge: | Translation-based strategies for cross-lingual transfer XLT include label projection . word aligners (WAs) are commonly used for label projection, but low-level design decisions for using them have not been investigated . |
| Approach: | They revisit word aligners (WAs) for label projection and propose a new projection strategy that outperforms WAs. |
| Outcome: | The proposed projection strategy outperforms marker-based methods in token classification tasks. |
TransAlign: Machine Translation Encoders are Strong Word Aligners, Too (2025.findings-emnlp)
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| Challenge: | translation-based approaches to cross-lingual transfer (XLT) are limited. |
| Approach: | They propose a word aligner that utilizes the encoder of a massively multilingual MT model. |
| Outcome: | The proposed word aligner outperforms existing WA and state-of-the-art non-WA-based methods in token classification tasks. |
Rationalize and Align: Enhancing Writing Assistance with Rationale via Self-Training for Improved Alignment (2025.findings-acl)
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| Challenge: | Existing writing assistants rely on supervised fine-tuning to optimize models for multiple revisions. |
| Approach: | They propose a framework that enhances WA performance with rationale and alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art WAs and the closed-source GPT-4o by 3.9 and 7.1 points on average across eight well-established writing-related test sets. |