Papers with lossy compression
Speechformer: Reducing Information Loss in Direct Speech Translation (2021.emnlp-main)
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| Challenge: | Current approaches to speech-to-text translation (ST) use a pipeline of two sub-components - an automatic speech recognition (ASR) and a machine translation (MT) model. |
| Approach: | They propose an architecture that avoids initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. |
| Outcome: | The proposed architecture achieves gains of up to 0.8 BLEU on the standard MuST-C corpus and up to 4.0 BLUE in a low resource scenario. |
Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization (2026.acl-long)
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| Challenge: | Recent work labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. |
| Approach: | They propose to use the Biasing Features metric to label a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. |
| Outcome: | The proposed metric confuses unfaithfulness with incompleteness, the authors argue . larger inference-time budgets greatly increase hint verbalization, they show . |