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Found 10 papers, 3 papers with code
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance
In the domain of Morphology, Inflection is a fundamental and important task that gained a lot of traction in recent years, mostly via SIGMORPHON’s shared-tasks. With average accuracy above 0. 9 over the scores of all languages, the task is considered mostly solved using relatively generic neural seq2seq models, even with little data provided. In this work, we propose to re-evaluate morphological inflection models by employing harder train-test splits that will challenge the generalization capacity of the models.
Well-Defined Morphology is Sentence-Level Morphology
Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context.
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features.
UniMorph 4.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study
In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, a multi-lingual repository of inflection tables.
Morphology Without Borders: Clause-Level Morphological Annotation
We use this dataset to derive 3 clause-level morphological tasks: inflection, reinflection and analysis.
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
The effect is most significant for low-resourced languages with a drop as high as 95 points, but even high-resourced languages lose about 10 points on average.
Minimal Supervision for Morphological Inflection
Neural models for the various flavours of morphological inflection tasks have proven to be extremely accurate given ample labeled data -- data that may be slow and costly to obtain.
Weakly Supervised Semantic Parsing with Abstract Examples
Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways.
Weakly-supervised Semantic Parsing with Abstract Examples
Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways.
