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# Hierarchical Phrase-Based Translation

**Authors:** [David Chiang](https://md.mova.elamur.ai/free/en/docs/papers/authors/david-chiang/) · **Year:** 2007 · **Venue:** Computational Linguistics **Link:** [aclanthology.org/J07-2003](https://aclanthology.org/J07-2003/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**1145** citations](https://openalex.org/works?filter=cites:W2437005631) openalex · 2026-10-19[**484** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/chiang-2007-hierarchical-phrase-based-translation/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J07-2/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J07-2003/)
- [PDF](https://aclanthology.org/J07-2003.pdf)
- [DOI 10.1162/coli.2007.33.2.201](https://doi.org/10.1162/coli.2007.33.2.201)
- [OpenAlex](https://openalex.org/W2437005631)

**Authors (1)**

1. [Chiang, David](https://openalex.org/A5036026526) [ORCID](https://orcid.org/0000-0002-0435-4864) · [Semantic Scholar](https://www.semanticscholar.org/author/145287425) · h 31 · 6918 citations · 162 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

This is the full version of the hierarchical model. Rules are extracted from a parallel text with no syntactic annotation, under length and nonterminal limits on the phrases they contain. The model is a synchronous context-free grammar with a log-linear score over features (phrase and lexical weights in both directions, a language model, rule counts and a word penalty). Decoding is a chart parser with cube pruning and k-best lists, and the language model is added in one of three ways, which are compared for time per sentence and BLEU on 400 development sentences. The system, Hiero, is written in Python with a just-in-time compiler. On the Chinese-English test sets, it beats the alignment template system in BLEU, and it beats a monotone version of itself, which is the nearest phrase-based equivalent.

## Ideas

- I1. Beam limits as an accuracy and time knob. For the encoder's choice of chunks, keep a beam of b candidates per cell, and report exact-match accuracy against beam size on dev verses. Stop at the beam where the residual no longer falls.
- I2. A monotone control. Run the encoder with no reordering, as Hiero Monotone does, to measure how much of the residual the order model removes. This is the honest baseline for open problem 7.
- I3. Length limits as parameters. Make the rule length and gap limits explicit parameters of the lexicon, and report the rule count and the residual for each setting, so the gain of a larger limit is shown with its cost.
- I4. Report decoding time per verse next to accuracy in the `tools/translate` README, in the same table, so that a gain bought with slow search is visible.

## For Mova

The code is `tools/translate` (`profile.rs` for the search over chunks, `measure.rs` for the table). The paper's search is a chart with pruning, which is deterministic if the beam ordering uses ordered maps. The monotone control is the cheapest check to add, and it is the first measure to take before any change to the search.

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

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/coli.2007.33.2.201](https://doi.org/10.1162/coli.2007.33.2.201)

```
@article{chiang2007hierarchical,
  title = {{Hierarchical Phrase-Based Translation}},
  author = {Chiang, David},
  journal = {Computational Linguistics},
  year = {2007},
  doi = {10.1162/coli.2007.33.2.201},
  url = {https://doi.org/10.1162/coli.2007.33.2.201}
}
```

- [← previous A Hierarchical Phrase-Based Model for Statistical Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/chiang-2005-hierarchical-phrase-based-model-statistical/)
- [next → Learning to Translate with Source and Target Syntax](https://md.mova.elamur.ai/free/en/docs/papers/chiang-2010-learning-translate-source-target-syntax/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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