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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 125 of 135 papers

TitleStatusHype
An Extended Sequence Tagging Vocabulary for Grammatical Error CorrectionCode4
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language ProcessingCode1
Applying the Transformer to Character-level TransductionCode1
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection EncodingCode1
Exact Hard Monotonic Attention for Character-Level TransductionCode1
Morphological Inflection with Phonological FeaturesCode0
Morphological Inflection Generation Using Character Sequence to Sequence LearningCode0
Neural Transition-based String Transduction for Limited-Resource Setting in MorphologyCode0
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level RulesCode0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
Morphological Inflection: A Reality CheckCode0
A Study of Morphological Robustness of Neural Machine TranslationCode0
An Encoder-Decoder Approach to the Paradigm Cell Filling ProblemCode0
Morphological Inflection Generation with Hard Monotonic AttentionCode0
Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 Shared TaskCode0
An Investigation of Noise in Morphological InflectionCode0
A Latent Morphology Model for Open-Vocabulary Neural Machine TranslationCode0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Falling Through the Gaps: Neural Architectures as Models of Morphological Rule LearningCode0
Minimal Supervision for Morphological InflectionCode0
IIT(BHU)--IIITH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological ReinflectionCode0
CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection GenerationCode0
A Structured Variational Autoencoder for Contextual Morphological InflectionCode0
A Framework for Bidirectional Decoding: Case Study in Morphological InflectionCode0
On Biasing Transformer Attention Towards MonotonicityCode0
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