33 papers with code • 0 benchmarks • 1 datasets
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
These leaderboards are used to track progress in Morphological Inflection
Most implemented papers
Pushing the Limits of Low-Resource Morphological Inflection
Recent years have seen exceptional strides in the task of automatic morphological inflection generation.
A Structured Variational Autoencoder for Contextual Morphological Inflection
Statistical morphological inflectors are typically trained on fully supervised, type-level data.
Exact Hard Monotonic Attention for Character-Level Transduction
Our models achieve state-of-the-art performance on morphological inflection.
Applying the Transformer to Character-level Transduction
The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks.
Systematic Inequalities in Language Technology Performance across the World's Languages
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development.
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms.
Morphological Inflection Generation Using Character Sequence to Sequence Learning
Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation.
Morphological Inflection Generation with Hard Monotonic Attention
We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection.
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology
We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection.
Finding the way from ä to a: Sub-character morphological inflection for the SIGMORPHON 2018 Shared Task
In this paper we describe the system submitted by UHH to the CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection.