Acquiring a Formality-Informed Lexical Resource for Style Analysis

What it is about

The paper builds a lexicon of German words ordered by formality, from vulgar and formal-register seeds up to elevated words. The seed list comes from existing lexicographic resources and is widened with sentence-based similarity. Crowd workers then place the enlarged set on a continuous informal-formal scale using best-worst scaling, a comparative method that needs fewer judgments than rating each word on its own. Split-half agreement is high, with a Spearman correlation of about 0.92. The lexicon serves as gold for regression models built on word embeddings: the best one reaches a cross-validated correlation of about 0.77. An extrinsic test scores the sentences of a German email corpus with the lexicon and reaches a correlation of about 0.73 with human formality labels. Scoring the whole range is harder than scoring the vulgar end alone, which the authors show by comparing with an earlier lexicon.

Ideas

• I1. Store a register score on each meta token: a concept-level value on a -1 to +1 scale, built from the source-language words that carry it. The encoder can then choose a chunk whose register matches the author's. This is the attribute open problem 8 asks for.
• I2. Use best-worst scaling to judge candidates. When two or three chunks compete for a slot, an annotator picks the best and the worst, which is cheaper than rating each. Report split-half reliability with each set of judgments.
• I3. Run an extrinsic check, as the paper does: score an author's corpus with the register lexicon and compare with the human label. The delta is credited only if the score moves in the right direction.
• I4. The regression is neural. The count form averages per-word scores over a chunk; build the lexicon from the Bible corpora and measure separately whether it tracks formality and whether it tracks archaism, since the two are different.

For Mova

The code is tools/translate (meta.rs for the register attribute, profile.rs for the chunk choice). The paper is in German, but the method is language-neutral and applies to English meta tokens. The lexicon-plus-extrinsic design addresses open problem 8 and gives a check that a style delta moves output in the right direction.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.eacl-main.174

@inproceedings{eder2021acquiring,
  title = {{Acquiring a Formality-Informed Lexical Resource for Style Analysis}},
  author = {Eder, Elisabeth and Krieg-Holz, Ulrike and Hahn, Udo},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.174},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.174}
}

References

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