Morphologically Aware Word-Level Translation

11/15/2020
by   Paula Czarnowska, et al.
0

We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information. This approach leads to substantial performance improvements - 19 improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16 contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/25/2022

Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies

Morphologically rich languages pose difficulties to machine translation....
research
10/25/2018

UniMorph 2.0: Universal Morphology

The Universal Morphology UniMorph project is a collaborative effort to i...
research
02/25/2022

Morphology Without Borders: Clause-Level Morphological Annotation

Morphological tasks use large multi-lingual datasets that organize words...
research
03/11/2021

Evaluation of Morphological Embeddings for the Russian Language

A number of morphology-based word embedding models were introduced in re...
research
08/30/2017

Paradigm Completion for Derivational Morphology

The generation of complex derived word forms has been an overlooked prob...
research
10/07/2016

Morphology Generation for Statistical Machine Translation using Deep Learning Techniques

Morphology in unbalanced languages remains a big challenge in the contex...
research
05/26/2023

Metaphor Detection via Explicit Basic Meanings Modelling

One noticeable trend in metaphor detection is the embrace of linguistic ...

Please sign up or login with your details

Forgot password? Click here to reset