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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 26–50 of 135 papers

TitleStatusHype
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection—0
Well-Defined Morphology is Sentence-Level Morphology—0
Backtranslation in Neural Morphological Inflection—0
Systematic Inequalities in Language Technology Performance across the World's LanguagesCode0
A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' PerformanceCode0
Improved pronunciation prediction accuracy using morphology—0
Were We There Already? Applying Minimal Generalization to the SIGMORPHON-UniMorph Shared Task on Cognitively Plausible Morphological Inflection—0
BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Training Strategies for Neural Multilingual Morphological Inflection—0
A Study of Morphological Robustness of Neural Machine TranslationCode0
What transfers in morphological inflection? Experiments with analogical models—0
Do RNN States Encode Abstract Phonological Alternations?—0
Falling Through the Gaps: Neural Architectures as Models of Morphological Rule LearningCode0
Minimal Supervision for Morphological InflectionCode0
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models—0
On Biasing Transformer Attention Towards MonotonicityCode0
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level RulesCode0
Do RNN States Encode Abstract Phonological Processes?—0
Smoothing and Shrinking the Sparse Seq2Seq Search SpaceCode0
Searching for Search Errors in Neural Morphological Inflection—0
Exploring Looping Effects in RNN-based Architectures—0
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models—0
Linguistically inspired morphological inflection with a sequence to sequence model—0
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