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
Benchmarks
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
LeenaShekhar/NLP-ML-Resources
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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
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ACL 2019
Our models achieve state-of-the-art performance on morphological inflection.
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.
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
sid7954/beam-joint-attention
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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
The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones.
