Morphological Inflection

26 papers with code • 0 benchmarks • 0 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

Most implemented papers

Pushing the Limits of Low-Resource Morphological Inflection

antonisa/inflection IJCNLP 2019

Recent years have seen exceptional strides in the task of automatic morphological inflection generation.

A Structured Variational Autoencoder for Contextual Morphological Inflection

LeenaShekhar/NLP-ML-Resources ACL 2018

Statistical morphological inflectors are typically trained on fully supervised, type-level data.

Exact Hard Monotonic Attention for Character-Level Transduction

shijie-wu/neural-transducer ACL 2019

Our models achieve state-of-the-art performance on morphological inflection.

Morphological Inflection Generation Using Character Sequence to Sequence Learning

mfaruqui/morph-trans NAACL 2016

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

roeeaharoni/morphological-reinflection ACL 2017

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

ZurichNLP/coling2018-neural-transition-based-morphology COLING 2018

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

nats/sigmorphon18 15 Sep 2018

In this paper we describe the system submitted by UHH to the CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection.

Surprisingly Easy Hard-Attention for Sequence to Sequence Learning

sid7954/beam-joint-attention EMNLP 2018

In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning.

An Encoder-Decoder Approach to the Paradigm Cell Filling Problem

mpsilfve/pcfp-data EMNLP 2018

The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones.