Improving Chinese Punctuation Restoration via External POS Tagger
Bibliographic record
Abstract
Automatic Speech Recognition (ASR) systems typically generate transcriptions without punctuation. To enhance readability and meet the expected input requirements for downstream language models, it's crucial to add punctuation marks in these transcripts. Most state-of-the-art improve the performance of punctuation restoration models by incorporating external information, such as part-of-speech(POS) tags. Although these models take POS tags into account, they predominantly focus on the English language, with limited research examining how POS tags enhance punctuation restoration performance in Chinese. In this paper, we validate the effectiveness of POS tags on the Chinese punctuation restoration task, and develop an innovative method to fusing POS tags with contextual embeddings. For English, we use the IWSLT dataset to verify the effectiveness of the fusion approach, while for Chinese we develop a new Chinese language dataset to evaluate the proposed methods. Experimental results show that our proposed method can consistently obtain performance gains, indicating its effectiveness for punctuation restoration tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".