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Found 11 papers, 1 papers with code
An Investigation of Noise in Morphological Inflection
We aim at closing this gap by investigating the types of noise encountered within a pipeline for truly unsupervised morphological paradigm completion and its impact on morphological inflection systems: First, we propose an error taxonomy and annotation pipeline for inflection training data.
Phonological Features for Morphological Inflection
Modeling morphological inflection is an important task in Natural Language Processing.
From Algebraic Word Problem to Program: A Formalized Approach
In this paper, we propose a pipeline to convert grade school level algebraic word problem into program of a formal languageA-IMP.
Multiple Instance Learning for Content Feedback Localization without Annotation
In contrast, we are interested in providing feedback specialized to the content of the essay, and specifically for the content areas required by the rubric.
Findings of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering
We describe the second SIGMORPHON shared task on unsupervised morphology: the goal of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering is to cluster word types from a raw text corpus into paradigms.
Paradigm Clustering with Weighted Edit Distance
This paper describes our system for the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering, which asks participants to group inflected forms together according their underlying lemma without the aid of annotated training data.
Morphological Processing of Low-Resource Languages: Where We Are and What's Next
Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages.
Morphological Processing of Low-Resource Languages: Where We Are and What’s Next
Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages.
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection
Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology.
