![]() ![]() BackgroundĪt the beginning of this project, we wrestled with three options for translating and coding the more than 11,000 non-English responses we collected about where people find meaning in life. In this post, we provide more background about this project and look more closely at a key question we faced along the way: whether to use professional translators or Google Translate to help make sense of the many thousands of open-ended responses we received. The analysis plan for this project hinged on developing a closed-ended codebook and applying it to nearly 19,000 open-ended responses, drawn from 17 societies and spanning 12 languages. Pew Research Center recently tackled this challenge for a cross-national survey in which people around the world described, in their own words, where they find meaning in life. Few research organizations are likely to have staff members who are fluent and well-versed enough in all of the relevant languages and cultures to analyze such responses with confidence. But they can present even bigger challenges when they are written in multiple languages. If words are missed out or translated incorrectly, there is often no way for a non-bilingual speaker to know, as the sentence will read well overall.Open-ended survey responses - in which respondents answer poll questions in their own words - are never easy for researchers to analyze. ![]() This can be potentially misleading for users as it obscures other errors. What’s more, the translations produced by Google often sound passably correct in the target language to non-native speakers (i.e. Otherwise, the translation may end up sounding humorous at best, and at worst its impact could be damaging, expensive or even life-threatening. If a text contains complex syntax, idioms or content which needs to be made suitable for a specific locale, a human translator who is sensitive to the linguistic nuances and cultural norms of the source and target languages and countries is required. On the other hand, if the user’s needs are a little more sophisticated or if the text is a little more nuanced, internet translation just won’t cut it. Moreover, the sentences used to test the GMNT were fairly short and simple, and are not of the complexity that professional translation companies deal with day-to-day.įor users wishing simply to understand basic language, such as tourists asking directions or requesting basic information, Google will probably translate the content pretty accurately and fulfil the purposes of the translation. However, for other language pairs, such as between English and Chinese, the standard remained lower despite some improvements. Google’s own research on the quality of its neural machine translation showed that the quality of translation between Indo-European languages was very high indeed. ![]() The quality of the translation very much depends on the language pair in question, the content of the text and what the translation is intended to be used for. But, (and this is a huge but), GNMT is still learning and does not pick up linguistic subtleties perceptible only to the human ear. Millions upon millions of contributions have been fed into the translation machine, which ultimately helps it learn more. The Translate Community is made up of volunteers who review translations and offer short translations of a few sentences. The Google Translate Community works on a similar basis to Google’s Local Guides who provide reviews and information such as opening times and prices for cafés, restaurants and attractions on Google. Google announced their own version of the system, Google Neural Machine Translation (GNMT), in September 2016, marking a significant advance on previous methods such as statistical-based machine translation. In a nutshell, NMT uses millions of sentences previously translated by humans to make links such as gender and number agreement between words at sentence level and then ‘learns’ how to translate similarly structured sentences in the future. Google Translate is based on both neural machine translation (NMT) and human contribution via its Translate Community.įully understanding NMT requires a bit of background knowledge about neural networks and deep learning. ![]()
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