Semantic differences in translation: Exploring the field of inchoativity

What it is about

The book asks whether translated language differs from non-translated language at the level of meaning, not only in lexis and grammar. The test case is inchoativity (starting or beginning) in Dutch: the verb beginnen and its neighbours, in the Dutch Parallel Corpus of about ten million words. The method is an extended Semantic Mirrors Method (SMM++). For a seed word it follows translations forward and back between languages, adds translation direction and frequency, and builds a candidate field of words that can then be clustered (correspondence analysis, then hierarchical clustering). Three fields are compared: original Dutch, Dutch translated from English, and Dutch translated from French. The reading is in terms of prototypes. The author describes changes as levelling out, shining through (source-language influence) and normalisation (target-language conformity). The changes are small and post hoc, and they show up more clearly on the word-sense level than on the lexeme level.

Ideas

• I1. Use the overlap test of the SMM as a merge check for the lexicon in tools/translate/src/meta.rs. Keep a concept merge only when the two words' sets of translations overlap above a threshold, in both directions. Test it on the known noisy merges (be with have, phrases with function words). Measure bits of reader+encoder residual with and without the check, twice, to see whether it removes the noise.
• I2. Add frequency counts to the translation pairs (the book's second extension). Store them as BTreeMap counts per concept pair, and compare the count-weighted merge rule with the current mutual most-frequent partners. Keep the old rule as the control.
• I3. Measure the author-level effects directly. For each author (KJV, WEB, ASV, JPS), count how often a concept is rendered with a word that the standard does not use (shining through), and how often a translated line moves to the standard's choice on a concept with a rare alternative (normalisation). This gives counts for the delta step in profile.rs and a check on open problem 8 (style deltas).
• I4. Negative control for any field-level claim: shuffle the translation sets of one author and rerun the clustering. If the cluster structure survives the shuffle, the result is an artefact of the procedure. The book itself warns that its visible changes come from post-hoc reading.

For Mova

The paper is the closest published method to the lexicon step of the translator: one concept should be one set of translation overlaps, not one word pair. Its direct use is open problem 2 (residual-driven lexicon), and its effect-counting is open problem 8 (style deltas per class). The method is count-based and fits the rules of Mova. The paper's field model is semantic and does not use trees, so it does not replace the meta-world reader.

Topics: Machine translation

Cite

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

@book{vandevoorde2020semantic,
  title = {{Semantic differences in translation: Exploring the field of inchoativity}},
  author = {Vandevoorde, Lore},
  year = {2020},
  url = {https://doi.org/10.5281/zenodo.2573677}
}

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