Morphological Inflection Edit Task
Natural Language Processing
About
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
Greatest papers with code
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing
LREC 2020
• CAMeL-Lab/camel_tools
• 
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
ARABIC SENTIMENT ANALYSIS ARABIC TEXT DIACRITIZATION DIALECT IDENTIFICATION MORPHOLOGICAL ANALYSIS MORPHOLOGICAL INFLECTION NAMED ENTITY RECOGNITION
Applying the Transformer to Character-level Transduction
• shijie-wu/neural-transducer
•
The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks.
Exact Hard Monotonic Attention for Character-Level Transduction
ACL 2019
• shijie-wu/neural-transducer
•
Our models achieve state-of-the-art performance on morphological inflection.
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.
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.
Pushing the Limits of Low-Resource Morphological Inflection
Recent years have seen exceptional strides in the task of automatic morphological inflection generation.
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
EMNLP 2018
• sid7954/beam-joint-attention
• 
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.
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages.
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic.