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

Most implemented papers

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.

SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin

a-quei/simplenlg-zh WS 2018

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

deep-spin/entmax ACL 2019

Sequence-to-sequence models are a powerful workhorse of NLP.

A Latent Morphology Model for Open-Vocabulary Neural Machine Translation

d-ataman/lmm ICLR 2020

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

salesforce/bite EMNLP 2020

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

CAMeL-Lab/camel_tools LREC 2020

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

sigmorphon2020/task0-data WS 2020

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

deep-spin/S7 NAACL 2021

Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.