New perspectives on cohesion and coherence: Implications for translation

What it is about

A volume on cohesion (the links between sentences: reference, conjunction, lexical repetition) and coherence (the logical flow of a text) in translated and multilingual texts, with a focus on empirical and computational methods. The chapters that matter here are these. Rysová studies Czech discourse connectives historically: the frozen primary connectives arose by grammaticalisation from particles, adverbs and prepositions. Rysová K. measures how information structure and text coreference relate in the Prague Dependency Treebank (about 86,600 coreference relations), and finds that coreference works mostly inside contextually bound items. Kerremans uses coreferential chains in English source texts to find term variants (for example climate change impact) and checks how their French and Dutch translations vary. Kunilovskaya and Kutuzov train a classifier on sentence-boundary windows of ten tokens and apply it to learner translations into Russian, to detect translationese. Lapshinova-Koltunski compares coreference and conjunction in English and German, original and translated, human and machine. Sim Smith and Specia build entity grids and entity graphs for English, French and German WMT and LIG texts. They find that transition probabilities differ by language, and that German compounds break entity identity, which compound splitting partly repairs.

Ideas

• I1. Build a per-sentence entity grid over the frames of prag: rows are entities (the arguments of a frame, keyed by concept), columns are sentences, cells are roles (subject, object, other, absent). Compute transition counts per author. Measure whether the transitions differ between the source text and the meta-world output of each Bible author, with a control on original English.
• I2. Split compounds before keying the lexicon. The paper shows that unsplit compounds fragment the chains. Keep a rule-based splitter for the concept key, with BTreeMap rules and a count of how many keys change. This touches open problem 2 (phrase keys).
• I3. A count-based translationese detector: a deterministic classifier over windows of UD tags and relations, trained on the reference corpus against translated Bible text. Report its accuracy on original English against original English, as a negative control. Its output is a style distance between authors, which can say where a delta is needed (open problem 6 and 8).
• I4. Count the explicit connectives per author and put the counts into the function-word features of the meta-tokens (open problem 4). The primary connectives are a closed list, so the count is cheap and fixed.

For Mova

The clearest use is the entity grid over prag frames: it gives coherence as numbers, computed on frames rather than surface words. The code is prag and tools/roundtrip for the frames, and tools/translate for the author comparison. The splitting idea applies to the Hebrew lexicon keys (words with prefixed particles) in tools/translate/src/meta.rs. The rest of the volume (Czech corpora, PDT-specific tools, learner data) does not carry over directly.

Topics: Coreference resolution · Machine translation

Cite

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

@book{kunz2017new,
  title = {{New perspectives on cohesion and coherence: Implications for translation}},
  author = {Kunz, Katrin Menzel, Ekaterina Lapshinova-Koltunski, Kerstin},
  year = {2017},
  url = {https://zenodo.org/records/814452}
}

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