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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

Showing 101135 of 135 papers

TitleStatusHype
POS induction with distributional and morphological information using a distance-dependent Chinese restaurant process0
An Investigation of Noise in Morphological InflectionCode0
Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 Shared TaskCode0
A Structured Variational Autoencoder for Contextual Morphological InflectionCode0
A Study of Morphological Robustness of Neural Machine TranslationCode0
Minimal Supervision for Morphological InflectionCode0
Pushing the Limits of Low-Resource Morphological InflectionCode0
Morphological Inflection: A Reality CheckCode0
Morphological Inflection Generation Using Character Sequence to Sequence LearningCode0
IIT(BHU)--IIITH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological ReinflectionCode0
An Encoder-Decoder Approach to the Paradigm Cell Filling ProblemCode0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
Sparse Sequence-to-Sequence ModelsCode0
Morphological Inflection Generation with Hard Monotonic AttentionCode0
CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection GenerationCode0
Morphological Inflection with Phonological FeaturesCode0
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' PerformanceCode0
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level RulesCode0
Surprisingly Easy Hard-Attention for Sequence to Sequence LearningCode0
Neural Transition-based String Transduction for Limited-Resource Setting in MorphologyCode0
Understanding Compositional Data Augmentation in Typologically Diverse Morphological InflectionCode0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological InflectionCode0
Systematic Inequalities in Language Technology Performance across the World's LanguagesCode0
On Biasing Transformer Attention Towards MonotonicityCode0
A Latent Morphology Model for Open-Vocabulary Neural Machine TranslationCode0
Systematic Inequalities in Language Technology Performance across the World’s LanguagesCode0
SIGMORPHON–UniMorph 2022 Shared Task 0: Modeling Inflection in Language AcquisitionCode0
OOVs in the Spotlight: How to Inflect them?Code0
SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological InflectionCode0
SimpleNLG-ZH: a Linguistic Realisation Engine for MandarinCode0
Falling Through the Gaps: Neural Architectures as Models of Morphological Rule LearningCode0
Smoothing and Shrinking the Sparse Seq2Seq Search SpaceCode0
A Framework for Bidirectional Decoding: Case Study in Morphological InflectionCode0
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