Papers › Alleviating the Inequality of Attention Heads for Neural Machine Translation
Alleviating the Inequality of Attention Heads for Neural Machine Translation
Zewei Sun, Shu-Jian Huang, Xin-yu Dai, Jia-Jun Chen
Recent studies show that the attention heads in Transformer are not equal. We relate this phenomenon to the imbalance training of multi-head attention and the model dependence on specific heads. To tackle this problem, we propose a simple masking method: HeadMask, in two specific ways. Experiments show that translation improvements are achieved on multiple language pairs. Subsequent empirical analyses also support our assumption and confirm the effectiveness of the method.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | IWSLT2015 Vietnamese-English | HeadMask (Random-18) | BLEU | 26.85 | #1 of 2 | Archive leaderboard | report |
| Machine Translation | IWSLT2015 Vietnamese-English | HeadMask (Impt-18) | BLEU | 26.36 | #2 of 2 | Archive leaderboard | report |
| Machine Translation | WMT2016 Romanian-English | HeadMask (Impt-18) | BLEU score | 32.95 | #10 of 21 | Archive leaderboard | report |
| Machine Translation | WMT2016 Romanian-English | HeadMask (Random-18) | BLEU score | 32.85 | #13 of 21 | Archive leaderboard | report |
| Machine Translation | WMT2017 Turkish-English | HeadMask (Random-18) | BLEU score | 17.56 | #1 of 2 | Archive leaderboard | report |
| Machine Translation | WMT2017 Turkish-English | HeadMask (Impt-18) | BLEU score | 17.48 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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