A survey of neural-network-based methods utilising comparable data for finding translation equivalents

TL;DR

This survey reviews neural-network-based methods for automatically inducing translation equivalents from comparable data, emphasizing the integration of NLP and lexicography perspectives to improve bilingual dictionary components.

Contribution

It provides a comprehensive analysis of neural-network approaches from a lexicographic viewpoint and highlights methods that combine NLP and lexicography for better translation equivalent induction.

Findings

Identifies key neural-network methods using comparable data.

Analyzes methods from a lexicographic perspective.

Suggests integration of NLP and lexicography enhances translation induction.

Abstract

The importance of inducing bilingual dictionary components in many natural language processing (NLP) applications is indisputable. However, the dictionary compilation process requires extensive work and combines two disciplines, NLP and lexicography, while the former often omits the latter. In this paper, we present the most common approaches from NLP that endeavour to automatically induce one of the essential dictionary components, translation equivalents and focus on the neural-network-based methods using comparable data. We analyse them from a lexicographic perspective since their viewpoints are crucial for improving the described methods. Moreover, we identify the methods that integrate these viewpoints and can be further exploited in various applications that require them. This survey encourages a connection between the NLP and lexicography fields as the NLP field can benefit from lexicographic insights, and it serves as a helping and inspiring material for further research in the context of neural-network-based methods utilising comparable data.