---
title: "Machine Translation with Inferred Stochastic Finite-State"
description: "The paper tackles automatic learning of translation models for restricted-domain tasks where parallel… 37 references, cited by 59 papers here."
authors: ["Francisco Casacuberta", "Enrique Vidal"]
year: 2004
venue: "Computational Linguistics"
source: "https://aclanthology.org/J04-2004/"
doi: "10.1162/089120104323093294"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/casacuberta-2004-machine-translation-inferred-stochastic-finite/"
---
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# Machine Translation with Inferred Stochastic Finite-State Transducers

**Authors:** [Francisco Casacuberta](https://md.research.elamur.ai/free/en/authors/francisco-casacuberta/) and [Enrique Vidal](https://md.research.elamur.ai/free/en/authors/enrique-vidal/) · **Year:** 2004 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2004](https://md.research.elamur.ai/free/en/venues/cl/2004/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/J04-2004/) · [PDF](https://aclanthology.org/J04-2004.pdf) · [DOI 10.1162/089120104323093294](https://doi.org/10.1162/089120104323093294) · [OpenAlex](https://openalex.org/W2132109814)

**Citations:** 148 citations (OpenAlex, 2026-10-14) · [59](https://md.research.elamur.ai/free/en/papers/casacuberta-2004-machine-translation-inferred-stochastic-finite/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles automatic learning of translation models for restricted-domain tasks where parallel sentence pairs are available. The authors use stochastic finite-state transducers because they are far cheaper to run than more expressive transduction models. Their inference method rests on a formal result: any (stochastic) rational translation is a homomorphic image of a (stochastic) regular language over a suitable extended alphabet. The method has three steps. First, statistical alignments turn each source–target pair into a single string of compound symbols. Next, an ordinary stochastic regular grammar, in practice an n-gram model with final states, is trained on these strings. Finally, each compound symbol is mapped back to its source/target components, which yields a transducer. Translation uses a Viterbi-style dynamic-programming approximation to the intractable exact search. The approach is tested in machine translation experiments within the EuTrans project. Its significance is that well-established grammar and n-gram learning tools can be reused directly to build translation transducers.

## Key points
- The method builds on Theorem 1 (Berstel 1979) and its stochastic extension, Theorem 2 (Casacuberta, Vidal, and Picó 2004). Together they guarantee that a transducer can be recovered from a regular language plus two morphisms, h_Σ and h_∆.
- Labeling function L maps training pairs to strings over an extended alphabet Γ, and an inverse function Λ restores them. Statistical alignment (e.g., Och and Ney 2000) guides the labeling.
- Exact search for the most probable translation is computationally hard (Casacuberta and de la Higuera 2000). Decoding instead maximizes the Viterbi score at a cost of O(|s|·|Q|·B), where B is the average branching factor.
- A toy Italian→English example ("una camera doppia") shows the approximation can fail. The pair with joint probability 0.6 loses to one with probability 0.4, because their Viterbi scores are 0.3 and 0.4.
- Experiments are framed within the EuTrans project. The extracted text omits the experimental section, so specific corpora statistics and error rates are not reported here.

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

## Institutions

- [Universitat Politècnica de València](https://md.research.elamur.ai/free/en/universities/universitat-politecnica-de-valencia/) — University, Valencia, Spain · 21 works · 3,275 citations

Also on the byline: [Centro Tecnológico de Investigación, Desarrollo e Innovación en tecnologías de la Información y las Comunicaciones (TIC)](https://ror.org/02s69db58).

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🧭 Topics (1): [🔁 Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) · 958 works

## Cite

Cite as: Francisco Casacuberta and Enrique Vidal. 2004. Machine Translation with Inferred Stochastic Finite-State Transducers. Computational Linguistics.

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

```bibtex
@article{casacuberta2004machine,
  title = {{Machine Translation with Inferred Stochastic Finite-State Transducers}},
  author = {Francisco Casacuberta and Enrique Vidal},
  journal = {Computational Linguistics},
  year = {2004},
  doi = {10.1162/089120104323093294},
  url = {https://doi.org/10.1162/089120104323093294}
}
```

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## References

37 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*