Machine Translation Divergences: A Formal Description and Proposed Solution
What it is about
Translation divergences are cross-linguistic differences where the same meaning comes out in a differently shaped sentence, so a direct structure-to-structure transfer breaks down. Dorr sets out seven lexical-semantic divergence types, illustrated with English, Spanish and German examples: thematic, promotional, demotional, structural, conflational, categorial and lexical. Each type is defined formally over lexical conceptual structures (LCS), which serve as the interlingua. A small set of general mapping routines plus per-language parameters, such as markers that promote or demote a head, produces the target syntax, so no language-pair transfer rules are needed. The paper argues that the classification covers all lexical-semantic divergences and that interacting divergences are handled uniformly. The text reports no corpus measurement; its support is the formal coverage argument and the UNITRAN prototype.
Ideas
- I1. Name the divergence classes in the measurement layer. For each aligned verse pair (for example KJV against WEB), tag each differing token pair with one of the seven types, using its UD head and relation, and count per class. This turns the residual per class that exact mode already reports into the paper's vocabulary. Where:
tools/translate,measure.rs, the classes as an enum. - I2. Head switching as a flag. When a source modifier becomes the main verb in the target (the promotional and demotional cases), keep one concept and record a promote or demote flag on the meta token, instead of minting a second concept. Where:
meta.rsand the reader's head patches, open problem 1. - I3. A full-coverage check, as the paper proposes. After encoding, count the source meta tokens that have no realisation in the target output. Shuffled targets must fail this count; that is the negative control.
- I4. Conflation as a test for phrase merges. The paper's example of one English verb against a two-word Spanish phrase is exactly the kind of merge that must not lose one side. Before a multi-word entry joins a concept, check that it lowers reader plus encoder bits (open problem 2).
For Mova
The paper is rule-based, so its ideas carry over without change. The places are tools/translate (meta.rs, measure.rs, profile.rs) and tools/roundtrip, whose divergence classes in normal mode should be checked against this list of seven. Nothing here is measured on text yet: each idea needs a count on the Bible pairs, run twice with identical output, before any claim.
Cite
@article{dorr1994machine,
title = {{Machine Translation Divergences: A Formal Description and Proposed Solution}},
author = {Dorr, Bonnie J.},
journal = {Computational Linguistics},
year = {1994},
url = {https://aclanthology.org/J94-4004/}
}References
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