Neural Lemmatization of Multiword Expressions
What it is about
A multiword expression (MWE) such as "cartes bleues" must keep the agreement of its parts when it is lemmatized: the lemma is "carte bleue", not "carte bleu". The authors lemmatize MWEs word by word. An encoder reads the characters of each word and the word sequence of the MWE, and an attention decoder generates the part of the lemma for each word. The task is not disambiguation, because one MWE form almost always has one lemma. The model is evaluated on Italian, French, Polish and Portuguese, with a focus on French and Polish, and it generalizes to unknown MWEs. In the ablation on French, the recurrent layer over the word sequence is described as crucial, and the knowledge of simple-word lemmas adds 7 to 8 points.
Ideas
- I1.
en/src/lexgen.rs: lemmatize English MWEs (phrasal verbs, compound nouns) part by part, with each word's lemma from the in-code dictionary and the agreement rule of the MWE as a table. Measure the MWE-level accuracy on the EWT test MWEs, and the share of MWEs whose lemma is wrong in the current lexicon. - I2. Run the paper's ablation as a table in Mova: remove the simple-word lemma knowledge, then the POS tags, then the position, and print the accuracy for each removal on EWT test.
- I3. Use the paper's MWE-level and word-level accuracy as two numbers in
en morph-eval, so one wrong word inside a correct MWE is visible.
For Mova
The code is en/src/lexgen.rs and en/src/morph.rs, with en morph-eval and en lexicon. The chapter is 19. The lexicon: vocabulary, multiword expressions and register. The paper has no English data and uses a neural model, so take the per-word method and the ablation plan only. Measure first on EWT test: MWE accuracy for the rule table, twice, against the held-out MWEs.
Cite
@inproceedings{schmitt2019neural,
title = {{Neural Lemmatization of Multiword Expressions}},
author = {Schmitt, Marine and Constant, Mathieu},
booktitle = {Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)},
year = {2019},
doi = {10.18653/v1/w19-5117},
url = {https://doi.org/10.18653/v1/w19-5117}
}References
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