---
title: "Redefining Machine Simultaneous Interpretation — Zhang 2026"
description: "Conventional simultaneous machine translation (SiMT) controls the trade-off between quality and latency using only READ/WRITE decisions."
authors: ["Qianen Zhang", "Zeyu Yang", "Satoshi Nakamura"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.611/"
doi: "10.18653/v1/2026.findings-acl.611"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-redefining-machine-simultaneous-interpretation-incremental/"
---
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# Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies

**Authors:** Qianen Zhang, [Zeyu Yang](https://md.research.elamur.ai/free/en/authors/zeyu-yang/), [Satoshi Nakamura](https://md.research.elamur.ai/free/en/authors/satoshi-nakamura/) · **Year:** 2026 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2026 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.611/) · [PDF](https://aclanthology.org/2026.findings-acl.611.pdf) · [DOI 10.18653/v1/2026.findings-acl.611](https://doi.org/10.18653/v1/2026.findings-acl.611) · [OpenAlex](https://openalex.org/W7166812841)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Conventional simultaneous machine translation (SiMT) controls the trade-off between quality and latency using only READ/WRITE decisions. Professional interpreters use richer tactics. This paper adds four interpreter-style operations to the action space of an LLM-based system: Sentence_Cut (splitting long clauses), Drop (removing fillers, repetitions and self-corrections), Partial_Summarization (condensing redundant clauses) and Pronominalization (replacing repeated noun phrases with unambiguous pronouns).

The pipeline has four stages:
- GPT-4o generates action sequences and translations on the ACL60/60 dev set.
- These outputs are used to compute per-action BLEU and latency statistics.
- The statistics are inserted into prompts so that Qwen3-8B can choose actions step by step during inference.
- Offline references are rewritten into action-adapted, SiMT-like training targets.

To measure delay in real time, the authors build a latency-aware TTS pipeline that yields TTS-based LAAL in seconds. On ACL60/60 En-Zh, En-De and En-Ja, the action-based systems improve semantic scores and reduce delay compared with salami-style baselines and with the original references. Combining Drop and Sentence_Cut gives the most consistent balance. The work presents SiMT as human-like, action-driven decoding rather than purely incremental translation.

## Key points
- **Baselines:** TransLLaMA and few-shot prompting, each with salami, reference or action-adapted supervision; a salami prompt; and the original references. A supervised check uses mBART50 on MuST-C tst-HE.
- **En-Zh results:**
  - Step-wise Drop/Cut reaches BLEU 62.84 with LAAL 2.118 s, compared with 2.848 s for the reference and 2.521 s for salami.
  - Step-wise with all actions has the highest COMET-da, 0.9002.
- **En-Ja results:** step-wise with all actions cuts LAAL to 2.524 s from 3.298 s for the reference.
- **En-De results:** step-wise Drop/Cut scores BLEU 49.97, versus 47.48 for salami. Its LAAL is about 1.18 s, versus 2.507 s for the reference.
- **Metric validation:** in En-Zh and En-De, TTS-based LAAL ranks systems the same way as token-level LAAL. Spearman word-order correlation ranks them differently, which suggests it captures other effects.
- **Limitations and availability:** the experiments are text-to-text only, with speech input left for future work. Code is released on GitHub (Cadente1/Action-based-Simultaneous-Machine-Translation).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) · [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026redefining,
  title = {{Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies}},
  author = {Zhang, Qianen and Yang, Zeyu and Nakamura, Satoshi},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.611},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.611}
}
```

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27. Sentence_Cut: Bei der Poolparty mit Barack Obama bekamen wir einen Graphen. Er hatte die richtigen Knoten zur Person und zum Ereignisthema, aber wir schätzten die zeitlichen Informationen falsch ein. [find](https://scholar.google.com/scholar?q=Sentence%5FCut%3A+Bei+der+Poolparty+mit+Barack+Obama+bekamen+wir+einen+Graphen%2E+Er+hatte+die+richtigen+Knoten+zur+Person+und+zum+Ereignisthema%2C+aber+wir+sch%C3%A4tzten+die+zeitlichen+Informationen+falsch+ein%2E)
28. Matthew Snover, Bonnie Dorr, Rich Schwartz, Linnea Micciulla, and John Makhoul. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 223–231, Cambridge, Massachu… [find](https://scholar.google.com/scholar?q=Matthew+Snover%2C+Bonnie+Dorr%2C+Rich+Schwartz%2C+Linnea+Micciulla%2C+and+John+Makhoul%2E+2006%2E+A+study+of+translation+edit+rate+with+targeted+human+annotation%2E+In+Proceedings+of+the+7th+Conference+of+the+Assoc) [our card](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/)
29. Zh. Reference: 在与Barack Obama的泳池 派对上，我们得到了一张关于人物和事 件主题正确节点的图表，但猜错了时间信 息。 Sentence_Cut: 在与Barack Obama的泳池派 对上，我们得到了一个图，标示了人物 和事件主题正确的节点，但猜错了时序信 息。 Minghan Wang, Thuy-Trang Vu, Jinming Zhao, Fatemeh Shiri, Ehsan Shareghi, and Gholamreza Haffari. 2024. Simultaneous machine translation with large langua… [find](https://scholar.google.com/scholar?q=Zh%2E+Reference%3A+%E5%9C%A8%E4%B8%8EBarack+Obama%E7%9A%84%E6%B3%B3%E6%B1%A0+%E6%B4%BE%E5%AF%B9%E4%B8%8A%EF%BC%8C%E6%88%91%E4%BB%AC%E5%BE%97%E5%88%B0%E4%BA%86%E4%B8%80%E5%BC%A0%E5%85%B3%E4%BA%8E%E4%BA%BA%E7%89%A9%E5%92%8C%E4%BA%8B+%E4%BB%B6%E4%B8%BB%E9%A2%98%E6%AD%A3%E7%A1%AE%E8%8A%82%E7%82%B9%E7%9A%84%E5%9B%BE%E8%A1%A8%EF%BC%8C%E4%BD%86%E7%8C%9C%E9%94%99%E4%BA%86%E6%97%B6%E9%97%B4%E4%BF%A1+%E6%81%AF%E3%80%82+Sentence%5FCut%3A+%E5%9C%A8%E4%B8%8EBarack+Obama%E7%9A%84%E6%B3%B3%E6%B1%A0%E6%B4%BE+%E5%AF%B9%E4%B8%8A%EF%BC%8C%E6%88%91%E4%BB%AC%E5%BE%97%E5%88%B0%E4%BA%86%E4%B8%80%E4%B8%AA%E5%9B%BE%EF%BC%8C%E6%A0%87%E7%A4%BA%E4%BA%86%E4%BA%BA%E7%89%A9+%E5%92%8C%E4%BA%8B%E4%BB%B6%E4%B8%BB%E9%A2%98%E6%AD%A3%E7%A1%AE%E7%9A%84%E8%8A%82%E7%82%B9%EF%BC%8C%E4%BD%86%E7%8C%9C%E9%94%99%E4%BA%86%E6%97%B6%E5%BA%8F%E4%BF%A1+%E6%81%AF%E3%80%82+Minghan+Wang%2C+Thuy%2DTrang+Vu%2C+Jinming+Zhao%2C+Fatemeh+Shi)
30. A.1.2 Source sentence: These are the morphology level, these are the morphology level embeddings. • De. Reference: Das hier ist die morphologische Ebene, das hier sind die Einbettungen auf der morphologischen Ebene. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Ch… [find](https://scholar.google.com/scholar?q=A%2E1%2E2+Source+sentence%3A+These+are+the+morphology+level%2C+these+are+the+morphology+level+embeddings%2E+%E2%80%A2+De%2E+Reference%3A+Das+hier+ist+die+morphologische+Ebene%2C+das+hier+sind+die+Einbettungen+auf+der+morphol) [our card](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
31. Drop: Das hier sind die Einbettungen auf der morphologischen Ebene. • Zh. Reference: 这些是形态学层面的，这 些是形态学层面的嵌入。 Jinming Zhao, Philip Arthur, Gholamreza Haffari, Trevor Cohn, and Ehsan Shareghi. 2021. It is not as good as you think! evaluating simultaneous machine translation on interpretation data. CoR… [find](https://scholar.google.com/scholar?q=Drop%3A+Das+hier+sind+die+Einbettungen+auf+der+morphologischen+Ebene%2E+%E2%80%A2+Zh%2E+Reference%3A+%E8%BF%99%E4%BA%9B%E6%98%AF%E5%BD%A2%E6%80%81%E5%AD%A6%E5%B1%82%E9%9D%A2%E7%9A%84%EF%BC%8C%E8%BF%99+%E4%BA%9B%E6%98%AF%E5%BD%A2%E6%80%81%E5%AD%A6%E5%B1%82%E9%9D%A2%E7%9A%84%E5%B5%8C%E5%85%A5%E3%80%82+Jinming+Zhao%2C+Philip+Arthur%2C+Gholamreza+Haffari%2C+Trevor+Cohn%2C+and+Ehsan+Shareghi%2E+2021%2E+It+)