Product and Process Analysis of Machine Translation into the Inflectional Language

Abstract

This study focuses on the influence of quality of Machine Translation (MT) output on a translator’s performance. We analyze the translator’s effort by product analysis and process analysis. The product analysis consists of MT quality evaluation according to the Dynamic Quality Framework; using error typology and the criteria such as fluency and adequacy. We examine translator’s effort from the point of view of typing time, in the context of MT quality—focusing on error rate in language, accuracy, terminology, and style, and also in fluency and adequacy to the source text. We have found that the translator’s performance is influenced by MT quality. The typing time is very closely related to errors in language, accuracy, terminology, and style as well as to fluency and adequacy. We used the Mann-Whitney test to compare the productivity of post-editing of MT with human translation. The results of the study have shown that post-editing—compared to human translation of journalistic text from English into the inflectional Slovak language is more effective.

Machine vs human translation: a new reality or a threat to professional Arabic–English translators

“…MT systems are fundamentally developed from HTs (Doherty, 2016). Furthermore, they are most effective in languages that are closely related and belong to the same family (which makes them at least a little similar) (Munkova et al, 2021). This is not our case because we have focused on Arabic as the source language and English as the target language.…”

“…Because MT systems are basically constructed from human translations (HTs), they help bridge the gap between human and MT. Today's systems often include millions of sentences translated by humans from which these systems gain probability patterns, while customized and freely accessible online systems can comprise even more data gathered from a huge number of translators over many years (Munkova et al, 2021). These systems are constantly improving in terms of consistency and effectiveness and more high-quality translation becomes available, posing a risk to human translators.…”

The use of residual analysis to improve the error rate accuracy of machine translation

“…The applied methodology, inspired by other studies 51 – 53 , consists of these stages (Fig. 1 ): Acquisition of unstructured textual data source text (journalistic texts).…”

Hasil Terjemahan Bahasa: Teknik Memasak Bali-Indonesia Melalui Aplikasi Facebook

“…Hasil penelitiannya bahwa hasil terjemahan peribahasa bahasa Inggris sebagai bahasa sumber ke dalam bahasa Indonesia sebagai bahasa sasaran menggunakan aplikasi mesin terjemahan dari google sudah bisa diterima dengan baik, walaupun ada beberapa yang masih kurang berterima maknanya. Mesin penerjemah atau yang biasa dipergunakan adalah google terjemahan dapat menghasilkan terjemahan yang mendekati penerjemah manusia asalkan memiliki struktur bahasa yang baik dan memiliki tanda baca yang tepat (Munkova et al, 2021;Palupi, 2019).…”

“…Penelitian tentang penerjemahan bahasa yang menggunakan mesin terjemahan sudah banyak dilakukan, seperti terjemahan ungkapan bahasa seksis dari bahasa Inggris ke dalam bahasa Indonesia dengan mesin terjemahan google (Palupi, 2019); akurasi hasil terjemahan peribahasa bahasa Inggris ke dalam bahasa Indonesia (Alawi, 2019); bias gender dalam mesin terjemahan (Stanovsky et al, 2019); ketidakkonsistensian penyampaian arti terjemahan oleh fitur terjemahan aplikasi facebook (Susanti & Ekasani, 2021); hasil terjemahan dengan mesin penerjemah lebih efektif dengan menggunakan proses post-editing (Munkova et al, 2021); tinjauan hasil terjemahan oleh mesin penerjemah (Fitria, 2021).…”