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Morphological Inflection

Morphological Inflection is the task of generating a target (inflected form) word from a source word (base form), given a morphological attribute, e.g. number, tense, and person etc. It is useful for alleviating data sparsity issues in translating morphologically rich languages. The transformation from a base form to an inflected form usually includes concatenating the base form with a prefix or a suffix and substituting some characters. For example, the inflected form of a Finnish stem eläkeikä (retirement age) is eläkeiittä when the case is abessive and the number is plural.

Source: Tackling Sequence to Sequence Mapping Problems with Neural Networks

Papers

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Showing 76–100 of 135 papers

TitleStatusHype
Inverting and Modeling Morphological Inflection—0
IPS-WASEDA system at CoNLL--SIGMORPHON 2018 Shared Task on morphological inflection—0
JRC-Names: A freely available, highly multilingual named entity resource—0
KU-CST at CoNLL--SIGMORPHON 2018 Shared Task: a Tridirectional Model—0
Learning to Learn Morphological Inflection for Resource-Poor Languages—0
Learning Transducer Models for Morphological Analysis from Example Inflections—0
Leveraging Principal Parts for Morphological Inflection—0
Linguistically inspired morphological inflection with a sequence to sequence model—0
Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars—0
Local String Transduction as Sequence Labeling—0
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings—0
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection—0
Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs—0
Morphological Inflection Generation with Multi-space Variational Encoder-Decoders—0
Morphological Reinflection in Context: CU Boulder's Submission to CoNLL--SIGMORPHON 2018 Shared Task—0
Morphology is not just a naive Bayes – UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection—0
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction—0
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models—0
Nomen Omen. Enhancing the Latin Morphological Analyser Lemlat with an Onomasticon—0
Not as Awful as it Seems: Explaining German Case through Computational Experiments in Fluid Construction Grammar—0
One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes—0
Online Segment to Segment Neural Transduction—0
Phonological Features for Morphological Inflection—0
POS induction with distributional and morphological information using a distance-dependent Chinese restaurant process—0
Posterior Attention Models for Sequence to Sequence Learning—0
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