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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 51–100 of 135 papers

TitleStatusHype
Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars—0
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection—0
Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection—0
The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer—0
SIGMORPHON 2020 Task 0 System Description: ETH Z\"urich Team—0
The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection—0
University of Illinois Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection—0
Transliteration for Cross-Lingual Morphological Inflection—0
Leveraging Principal Parts for Morphological Inflection—0
The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2—0
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological InflectionCode0
Applying the Transformer to Character-level TransductionCode1
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language ProcessingCode1
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation—0
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection EncodingCode1
Learning to Learn Morphological Inflection for Resource-Poor Languages—0
A Resource for Studying Chatino Verbal Morphology—0
Surface Realisation Using Full Delexicalisation—0
The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization—0
A Latent Morphology Model for Open-Vocabulary Neural Machine TranslationCode0
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection—0
Ensembles of Neural Morphological Inflection Models—0
Pushing the Limits of Low-Resource Morphological InflectionCode0
Cross-lingual morphological inflection with explicit alignment—0
Inverting and Modeling Morphological Inflection—0
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few—0
Exact Hard Monotonic Attention for Character-Level TransductionCode1
Sparse Sequence-to-Sequence ModelsCode0
Contextualization of Morphological Inflection—0
Posterior Attention Models for Sequence to Sequence Learning—0
SimpleNLG-ZH: a Linguistic Realisation Engine for MandarinCode0
Phonological Features for Morphological Inflection—0
KU-CST at CoNLL--SIGMORPHON 2018 Shared Task: a Tridirectional Model—0
UZH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection—0
What can we gain from language models for morphological inflection?—0
BME-HAS System for CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection—0
IIT(BHU)--IIITH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological ReinflectionCode0
Finding the way from \"a to a: Sub-character morphological inflection for the SIGMORPHON 2018 shared task—0
IPS-WASEDA system at CoNLL--SIGMORPHON 2018 Shared Task on morphological inflection—0
T\"ubingen-Oslo system at SIGMORPHON shared task on morphological inflection. A multi-tasking multilingual sequence to sequence model.—0
Morphological Reinflection in Context: CU Boulder's Submission to CoNLL--SIGMORPHON 2018 Shared Task—0
The NYU System for the CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection—0
Experiments on Morphological Reinflection: CoNLL-2018 Shared Task—0
Surprisingly Easy Hard-Attention for Sequence to Sequence LearningCode0
An Encoder-Decoder Approach to the Paradigm Cell Filling ProblemCode0
Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 Shared TaskCode0
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding—0
Neural Transition-based String Transduction for Limited-Resource Setting in MorphologyCode0
Fast and Accurate Reordering with ITG Transition RNN—0
Local String Transduction as Sequence Labeling—0
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