A Simple Joint Model for Improved Contextual Neural Lemmatization

What it is about

Lemmatization maps an inflected form to its citation form, and the right lemma often depends on context, as "running" is a verb in one sentence and a noun in another. The paper proposes a joint neural model that predicts the morphological tag of each word and then generates the lemma conditioned on the tag and on the sentence, decoded either greedily or by crunching over the k best tag sequences. On 20 UD languages it reaches 95.42 average lemma accuracy against 95.05 for the strongest baseline (Bergmanis and Goldwater), a difference the authors report as significant. The gains grow as the treebank gets smaller, with a correlation of -0.845 between training tokens and the improvement, and feeding gold tags to the model shows that better tagging would raise lemma accuracy further.

Ideas

• I1. Tag-conditioned lemma rules in en/src/morph.rs: when the dictionary has several candidates or a suffix rule is ambiguous, choose the rule with the UPOS and FEATS predicted by en/src/ud.rs; measure lemma accuracy on EWT dev, reporting ambiguous tokens (more than one lemma) separately from the rest.
• I2. Crunching in en: keep the k best UPOS and FEATS per token and pick the lemma with the best joint score; measure lemma accuracy on EWT dev against the greedy choice, bank by bank, since the paper's gain is significant only for some languages.
• I3. Low-resource slope: train the lemma choice on 10, 25, 50 and 100 percent of the English training data and plot accuracy, as the paper's learning curve does; the claim to test is that tag-conditioned lemmas gain most with little data (cv on train, EWT test once per release).

For Mova

The lemmatizer is en/src/morph.rs (dictionary from AGID and UD plus suffix rules, lemma accuracy 99.3 to 99.5 percent), and the tags come from en/src/ud.rs. The chapter is 10. Word classes, with 13. Verbs for the verb forms. Measure first the lemma accuracy on ambiguous tokens of EWT dev, where the paper's effect would show.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/n19-1155 · arXiv 1904.02306

@inproceedings{malaviya2019simple,
  title = {{A Simple Joint Model for Improved Contextual Neural Lemmatization}},
  author = {Malaviya, Chaitanya and Wu, Shijie and Cotterell, Ryan},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  year = {2019},
  doi = {10.18653/v1/n19-1155},
  eprint = {1904.02306},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n19-1155}
}

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