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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 101–125 of 135 papers

TitleStatusHype
The OSU Realizer for SRST `18: Neural Sequence-to-Sequence Inflection and Incremental Locality-Based Linearization—0
The DipInfo-UniTo system for SRST 2018—0
A Structured Variational Autoencoder for Contextual Morphological InflectionCode0
Using English Baits to Catch Serbian Multi-Word Terminology—0
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings—0
Inflection Generation for Spanish Verbs using Supervised Learning—0
Character-based recurrent neural networks for morphological relational reasoning—0
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary—0
Training Data Augmentation for Low-Resource Morphological Inflection—0
Experiments on Morphological Reinflection: CoNLL-2017 Shared Task—0
Seq2seq for Morphological Reinflection: When Deep Learning Fails—0
Morphological Inflection Generation with Multi-space Variational Encoder-Decoders—0
SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models—0
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction—0
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings—0
The Neural Noisy Channel—0
Morphological Inflection Generation with Hard Monotonic AttentionCode0
Online Segment to Segment Neural Transduction—0
Learning Transducer Models for Morphological Analysis from Example Inflections—0
Improving Sequence to Sequence Learning for Morphological Inflection Generation: The BIU-MIT Systems for the SIGMORPHON 2016 Shared Task for Morphological Reinflection—0
Nomen Omen. Enhancing the Latin Morphological Analyser Lemlat with an Onomasticon—0
Using longest common subsequence and character models to predict word forms—0
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection—0
Analyzing Learner Understanding of Novel L2 Vocabulary—0
Morphological Inflection Generation Using Character Sequence to Sequence LearningCode0
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