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

**Authors:** Michele Bevilacqua, Marco Maru, [Roberto Navigli](https://md.mova.elamur.ai/free/en/docs/papers/authors/roberto-navigli/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) **Link:** [aclanthology.org/2020.emnlp-main.585](https://aclanthology.org/2020.emnlp-main.585/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**43** citations](https://openalex.org/works?filter=cites:W3104336196) openalex · 2026-10-19[**48** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lex-2020-bevilacqua-generationary/) of the 140673 papers whose bibliographies we read[EMNLP 2020](https://openalex.org/S4363608991)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://doi.org/10.18653/v1/2020.emnlp-main.585)
- [PDF](https://aclanthology.org/2020.emnlp-main.585.pdf)
- [DOI 10.18653/v1/2020.emnlp-main.585](https://doi.org/10.18653/v1/2020.emnlp-main.585)
- [OpenAlex](https://openalex.org/W3104336196)

**Authors (3)**

1. [Michele Bevilacqua](https://openalex.org/A5079283776) [ORCID](https://orcid.org/0000-0002-2717-1454) · [Semantic Scholar](https://www.semanticscholar.org/author/143802044) · h 10 · 608 citations · 25 works
2. [Marco Maru](https://openalex.org/A5037855689) [Semantic Scholar](https://www.semanticscholar.org/author/1388826344) · h 6 · 754 citations · 19 works
3. [Roberto Navigli](https://openalex.org/A5026648049) [ORCID](https://orcid.org/0000-0003-3831-9706) · [Semantic Scholar](https://www.semanticscholar.org/author/1733928) · h 61 · 19197 citations · 555 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## 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

- I1. `onto/src/wsd.rs`: handle a target that is not in WordNet (an adjective-noun phrase, for example) by taking the sense of its head noun and adding the modifier's synonym set as a restriction. Measure the accuracy on adjective-noun phrases in EWT that have no WordNet entry, against the head-only baseline.
- I2. Multi-inventory candidates: put the WordNet, FrameNet and Wikidata glosses of a word into one candidate pool in `onto`, and keep the source of each chosen gloss in the explanation. Measure F1 on the Raganato sets (test only), per inventory and for the pool, and check that two runs are identical.
- I3. Offline judge: `annot` produces alternative glosses for a sample of targets, and a Rust check keeps only those that contain the target lemma's WordNet synonym or hypernym. Use this as silver data only; the paper's 4.3-point zero-shot gain is a neural result and is not a target for Mova.

## 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](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/), with [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) for the phrase targets. Measure first the head-noun fallback on adjective-noun phrases that have no WordNet entry.

Topics: [Word sense disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/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](https://doi.org/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}
}
```

- [← previous English WordNet 2019 – An Open-Source WordNet for English](https://md.mova.elamur.ai/free/en/docs/papers/lex-2019-mccrae-english-wordnet/)
- [next → Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods](https://md.mova.elamur.ai/free/en/docs/papers/lex-2020-ryskina-neologisms-distributional/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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