Valintaverkkoanalyysi konekäännöksen jälkieditoinnin tarkastelun apuna

Authors

• Leena Salmi Turun yliopisto
• Maarit Koponen Turun yliopisto & Helsingin yliopisto

DOI:

https://doi.org/10.61200/mikael.129302

Keywords:

Machine Translation, post-editing, Choice Network Analysis

Abstract

This paper presents an application of Choice Network Analysis (CNA; proposed by Stuart Campbell in 2000) to analyse Machine Translation (MT) post-editing (PE) data. The data were collected from 33 translation students in a PE experiment where the students post-edited a text machine translated from English to Finnish. The text was a combination of neural (NMT), rule-based (RBMT) and statistical machine translation (SMT) output.
CNA has been presented as a method for collecting data on the mental process of translating (or post-editing), to be used either instead of or in addition to experimental process studies. The method consists of collecting translations of the same text from different translators and building a network of the different options they have used to translate a particular item. The basic assumption is that when an item is translated in the same or a similar way, it requires less cognitive effort than an item that produced several different translations.
In this paper, we use CNA to analyse a subset of the PE data, items that are repeated in the post-edited text, to compare differences both between editors and between MT engines. We also discuss two different approaches to presenting the choice networks.