So far, independent MT comparisons have been hard to come by. In 2016 – 2017 new entrants to NMT have posted numerous press-releases stating they’ve approached human quality (Google 2016, Microsoft 2018), or beat competitors (Lilt 2017, taken down since, DeepL 2017). Most of these comparisons have relied on a variation BLEU metric, which compares machine translation with a pre-loaded human translation, and can be manipulated.
At the same time, the need for independent evaluation is rising.
One Hour Translation launched a human machine translation evaluation service, ONEs (OHT NMT Evaluation Score). It opened up with an infographic comparing Google, Neural Systran and DeepL in 4 language combinations.
OHT’s approach is to have 20 human evaluators per language combination, all with experience in the subject area. Each scores the translations sentence by sentence from 1 to 100 with instructions regarding what each degree means. Tests are blind – translators do not know which engine they are evaluating, and thus carry no bias. Two statistical tests verify human inputs – the first one to ensure the results statistically significant, and the second to calculate the confidence score and margin of error. In the reporting section, OHT slices the results by winning sentences per engine, valuations for each sentence, and score distribution.
Evaluations take about two days to compile, and cost in the range of USD 2,000 – 4,000 depending on the number of sentences, languages, and engines compared.
This human evaluation can work for any technology, including RbMT, SMT, and NMT, as well as public and on-premise MT.
Inten.to has launched an automatic evaluation with a marketplace. They made the first appearance in mid-2017 and since then got funded for close to USD 1 million.
Inten.to compares engines by quality and price on a quarterly basis, automatically selects the most suitable for the current task, and then provides that engine via API. Inten.to monetizes by selling MT with a markup.
For quality evaluations Inten.to uses LEPOR scores, a derivative of BLEU that compares MT with reference human translations.
So far, Inten.to has been integrated into Smartcat and Fluency TMS, where users can select its aggregation just like a regular engine.
These two new offerings usher in a new market niche of MT selection.
There still remain plenty of opportunities. For instance, there isn’t:
While rapidly progressing towards end-users, MT training and comparison remains expert-driven and somewhat academic in flavor. In the next 3 years however, all that could change.
Blockchain and bitcoin have been around for more than a decade now, but most of us mortals are still struggling to understand how all this technology comes together, without even considering its implications for the language services industry. Let’s give it a try.
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In this episode of Globally Speaking, we chat with Peter Wang, Founder and CEO of 57Blocks, about his experience as a serial entrepreneur creating startups whose workforce is a distributed team model. He explains best practices for sourcing and managing distributed teams, how to earn their trust and what the hot new markets for talent will be. Plus, he reveals some of his own lessons learned about building products differently for new markets and adapting to the current self-serve software business model.