Morphological Inflection
37 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
Benchmarks
These leaderboards are used to track progress in Morphological Inflection
Most implemented papers
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
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.
SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin
We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009).
Sparse Sequence-to-Sequence Models
Sequence-to-sequence models are a powerful workhorse of NLP.
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.
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English.
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
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.
Smoothing and Shrinking the Sparse Seq2Seq Search Space
Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.