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Generationary or «How We Went beyond Word Sense Inventories and Learned to Gloss»

What it is about

The paper replaces the choice from a sense inventory with generation of a definition for a word in its context. Generationary fine-tunes BART with a span-based encoding that marks the target, which allows targets of any length, including unseen multi-word expressions. It is trained on data from several sense inventories at once. On the Raganato et al. framework of WSD tests, the single-inventory model is comparable to GlossBERT, and the multi-inventory model gives up 0.4 F1 points on the full ALL set and 0.2 on ALL minus the dev set. In exchange it gains about 8 points on the zero-shot set, where it beats GlossBERT by 4.3 points. The authors also release a set of definitions for free adjective-noun phrases, called Hei++, and in human judgement the generated definitions are often equal to or better than lexicographers' ones.

Ideas

For Mova

The touch points are onto/src/wsd.rs (the sense choice with explanations), onto lexical data (WordNet, FrameNet) and the annot teacher side, which may produce glosses offline but never answers at test time. The Raganato test sets are for tests only. The chapter is 19. The lexicon: vocabulary, multiword expressions and register, with 22. Phrases: noun, adjective, adverb and prepositional phrases for the phrase targets. Measure first the head-noun fallback on adjective-noun phrases that have no WordNet entry.

Topics: Word sense disambiguation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.emnlp-main.585

@inproceedings{bevilacqua2020generationary,
  title = {{Generationary or «How We Went beyond Word Sense Inventories and Learned to Gloss»}},
  author = {Bevilacqua, Michele and Maru, Marco and Navigli, Roberto},
  booktitle = {EMNLP 2020},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.585},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.585}
}

References

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