Papers with English-Italian
ESCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing (L18-1)
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| Challenge: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
| Approach: | a team of researchers develops a Synthetic Corpus for Automatic Post-Editing . eSCAPE is the largest freely-available Synthetic corpus for automatic post-editing released so far . the results prove that the models always improve MT quality with statistically significant gains . |
| Outcome: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
CTC-based Compression for Direct Speech Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). |
| Approach: | They propose a method which performs a phone-informed compression of the input audio in direct ST models by exploiting the Connectionist Temporal Classification (CTC) they demonstrate that their method brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German) |
| Outcome: | The proposed method brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German) it reduces memory footprint by more than 10%, and is faster than previous approaches. |
Real Men are Tough: Evaluating Gender Bias and Sensitivity to Masculinity Norms in LLMs (2026.findings-acl)
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| Challenge: | Large language models exhibit gender bias, but most evaluations focus on downstream stereotypes . a recent study found that explicit endorsement of masculinity norms is low across models . |
| Approach: | They investigate whether large language models rely on traditional masculinity norms as latent priors in gender-biased inference. |
| Outcome: | The findings show that large language models rely on stereotypes as latent priors . the authors used the Male Role Norms Inventory (MRNI) to investigate gender bias . |
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation (2021.findings-acl)
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| Challenge: | Existing methods for subword splitting penalize the representation of feminine linguistic markings. |
| Approach: | They propose a method that preserves subword splitting while leveraging character-based segmentation to properly translate gender. |
| Outcome: | The proposed approach preserves BPE overall translation quality while leveraging the higher ability of character-based segmentation to properly translate gender. |
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus (2020.acl-main)
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| Challenge: | a growing number of studies have examined the issue of gender bias in speech translation . a gender bias is a systemic problem that reproduces gender stereotypes discriminating women. |
| Approach: | They present the first thorough investigation of gender bias in speech translation . they compare audio technologies for English-Italian/French translations . |
| Outcome: | The proposed method compares different technologies on two languages, English and French. |