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. We evaluate the model on three previously studied morphological inflection generation datasets and show that it provides state of the art results in various set...

We propose a novel language-independent framework for inducing a collection of morphological inflection classes from a monolingual corpus of full form words. Our approach involves two main stages. In the first stage, we generate a large data structure of candidate inflection classes and their interrelationships. In the second stage, search and filtering techniques are applied to this data struc...

We improve the quality of statistical machine translation (SMT) by applying models that predict word forms from their stems using extensive morphological and syntactic information from both the source and target languages. Our inflection generation models are trained independently of the SMT system. We investigate different ways of combining the inflection prediction component with the SMT syst...

Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervis...

Extracting and performing an alignment of the longest common subsequence in inflection tables has been shown to be a fruitful approach to supervised learning of morphological paradigms. However, finding the longest subsequence common to multiple strings is well known to be an intractable problem. Additional constraints on the solution sought complicate the problem further—such as requiring that...

This study aims to compare the predictive capability of two popular software reliability growth models (SRGM), say exponential growth and inflection S-shaped growth models. We first review the exponentiated Weibull (EW) testing-effort functions and discuss exponential type and inflection S-shaped type SRGM with EW testing-effort. We then analyzed the actual data applications and compare the pre...

We obtain the distribution of inflection points and singularities on a parametric rational cubic curve segment with aid of Mathematica (A System of for Doing Mathematics by Computer). The reciprocal numbers of the magnitudes of the end slopes determine the occurrence of inflection points and singularities on the segment. Its use enables us to check whether the segment has inflection points or a...