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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 1–50 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
Exact Hard Monotonic Attention for Character-Level TransductionCode1
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection EncodingCode1
Applying the Transformer to Character-level TransductionCode1
Morphological Inflection with Phonological FeaturesCode0
Surprisingly Easy Hard-Attention for Sequence to Sequence LearningCode0
Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 Shared TaskCode0
Systematic Inequalities in Language Technology Performance across the World’s LanguagesCode0
SimpleNLG-ZH: a Linguistic Realisation Engine for MandarinCode0
IIT(BHU)--IIITH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological ReinflectionCode0
Smoothing and Shrinking the Sparse Seq2Seq Search SpaceCode0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
Falling Through the Gaps: Neural Architectures as Models of Morphological Rule LearningCode0
Understanding Compositional Data Augmentation in Typologically Diverse Morphological InflectionCode0
Neural Transition-based String Transduction for Limited-Resource Setting in MorphologyCode0
Morphological Inflection Generation with Hard Monotonic AttentionCode0
On Biasing Transformer Attention Towards MonotonicityCode0
Pushing the Limits of Low-Resource Morphological InflectionCode0
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological InflectionCode0
Sparse Sequence-to-Sequence ModelsCode0
Systematic Inequalities in Language Technology Performance across the World's LanguagesCode0
Minimal Supervision for Morphological InflectionCode0
A Structured Variational Autoencoder for Contextual Morphological InflectionCode0
A Study of Morphological Robustness of Neural Machine TranslationCode0
CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection GenerationCode0
An Encoder-Decoder Approach to the Paradigm Cell Filling ProblemCode0
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level RulesCode0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Morphological Inflection: A Reality CheckCode0
OOVs in the Spotlight: How to Inflect them?Code0
A Latent Morphology Model for Open-Vocabulary Neural Machine TranslationCode0
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' PerformanceCode0
An Investigation of Noise in Morphological InflectionCode0
Morphological Inflection Generation Using Character Sequence to Sequence LearningCode0
Do RNN States Encode Abstract Phonological Alternations?—0
Autoregressive Modeling with Lookahead Attention—0
Do RNN States Encode Abstract Phonological Processes?—0
DeLex, a freely-avaible, large-scale and linguistically grounded morphological lexicon for German—0
A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Cross-lingual morphological inflection with explicit alignment—0
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding—0
Controlled Ascent: Imbuing Statistical MT with Linguistic Knowledge—0
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection—0
Contextualization of Morphological Inflection—0
Analyzing Learner Understanding of Novel L2 Vocabulary—0
A Framework for Bidirectional Decoding: Case Study in Morphological Inflection—0
Improved pronunciation prediction accuracy using morphology—0
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation—0
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