Papers › Sequence Generation with Mixed Representations
Sequence Generation with Mixed Representations
Lijun Wu Shufang Xie Yingce Xia Fan Yang Tao Qin Jianhuang Lai Tie-Yan Liu
Tokenization is the first step of many natural language processing (NLP) tasks and plays an important role for neural NLP models. Tokenizaton method such as byte-pair encoding (BPE), which can greatly reduce the large vocabulary and deal with out-of-vocabulary words, has shown to be effective and is widely adopted for sequence generation tasks. While various tokenization methods exist, there is no common acknowledgement which is the best. In this work, we propose to leverage the mixed representations from different tokenization methods for sequence generation tasks, in order to boost the model performance with unique characteristics and advantages of individual tokenization methods. Specifically, we introduce a new model architecture to incorporate mixed representations and a co-teaching algorithm to better utilize the diversity of different tokenization methods. Our approach achieves significant improvements on neural machine translation (NMT) tasks with six language pairs (e.g., English↔German, English↔Romanian), as well as an abstractive summarization task.
Code
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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 | IWSLT2014 English-German | MixedRepresentations | BLEU score | 29.93 | #6 of 6 | Archive leaderboard | report |
| Machine Translation | IWSLT2014 German-English | MixedRepresentations | BLEU score | 36.41 | #14 of 34 | 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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