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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
Applying the Transformer to Character-level TransductionCode1
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language ProcessingCode1
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection EncodingCode1
Exact Hard Monotonic Attention for Character-Level TransductionCode1
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives—0
Probing Subphonemes in Morphology Models—0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
OOVs in the Spotlight: How to Inflect them?Code0
Exploring Linguistic Probes for Morphological Generalization—0
Morphological Inflection with Phonological FeaturesCode0
An Investigation of Noise in Morphological InflectionCode0
Morphological Inflection: A Reality CheckCode0
Understanding Compositional Data Augmentation in Typologically Diverse Morphological InflectionCode0
A Framework for Bidirectional Decoding: Case Study in Morphological InflectionCode0
Autoregressive Modeling with Lookahead Attention—0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs—0
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection—0
SIGMORPHON–UniMorph 2022 Shared Task 0: Modeling Inflection in Language AcquisitionCode0
Morphology is not just a naive Bayes – UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection—0
SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches—0
Generalizing Morphological Inflection Systems to Unseen Lemmas—0
CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection GenerationCode0
SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological InflectionCode0
UniMorph 4.0: Universal Morphology—0
One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes—0
Systematic Inequalities in Language Technology Performance across the World’s LanguagesCode0
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance—0
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection—0
Backtranslation in Neural Morphological Inflection—0
Well-Defined Morphology is Sentence-Level Morphology—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
A Study of Morphological Robustness of Neural Machine TranslationCode0
Improved pronunciation prediction accuracy using morphology—0
Training Strategies for Neural Multilingual Morphological Inflection—0
What transfers in morphological inflection? Experiments with analogical models—0
BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Were We There Already? Applying Minimal Generalization to the SIGMORPHON-UniMorph Shared Task on Cognitively Plausible Morphological Inflection—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
Do RNN States Encode Abstract Phonological Processes?—0
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level RulesCode0
Smoothing and Shrinking the Sparse Seq2Seq Search SpaceCode0
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