Responsive Machine Translation and the Search for Metadata
Metadata is familiar enough of a concept to the average person. We know it from movie metadata (director, release date, actors, etc.) and book metadata (author, cover photo, font, ISBN, etc.). Even if one is not familiar with the concept of metadata, the word itself is self-explanatory. “Meta,” from the Ancient Greek for “after”, takes on the meaning of the preposition “about” in modern parlance. Therefore, metadata refers to “data about data”: secondary data that informs the user about the main data at hand.
Metadata is a potent tool for customizing machine translation to fit the nuanced needs of the user, but not many people know about this. After all, machine translation is a relatively arcane and scientific area of expertise and not the most user-friendly for the average language industry worker. “Media and game localizers may not be taking full advantage of the cost and efficiency benefits offered by MT because translation work is still constrained by the limits of popular black-box MT systems,” writes AppTek in a 2021 article on Slator. Many people don’t quite understand how MT systems work, and as a result, they waste time and effort building models after models, engines after engines, doing pretty much the same things, except in a different context.
But AppTek has a solution; they’re working on MT systems that can be fitted with better metadata to provide more detailed, customizable translations to their users. “Metadata can be leveraged in the post-editing workflow to increase quality and boost productivity,” they advertise. AppTek challenges us to think outside the (black) box: “what if instead the register of the language required in each case could be taken care of by the same model…?” What if we could, at the flip of a switch, modify the tone, diction, and mood of the speaker? What if we could clarify that the recipient of a translation was a certain gender, class, or position? There would no longer be a need for separate translations each time; no need for building completely new systems for every single register or tone.
This is Apptek’s new idea: MT that can be augmented with different types of metadata to better accommodate a wider range of linguistic nuances. In a white paper they published in 2020, AppTek details the kind of changes metadata can offer to a static MT system. They outline 8 specific metadata categories that can radically facilitate machine translation users:
- formal, informal
- for example, tu/usted (Spanish “you”) or Sie/ihr (German “you”) distinctions in the second person, and corresponding grammatical inflections
- Speaker Gender
- male, female, nonbinary
- corresponding grammatical inflections and contextual understanding of the text
- Domain or Genre
- data regarding tone or language depending on the form of the content (news, entertainment, talks, etc.)
- e.g. Essen vs. Nahrung (“food”) in German
- caters to “more specific document-level style and terminology differences”
- allows MT systems to understand the context, thereby allowing for more accurate translation for ambiguous word choices
- users will have the ability to control the length of the translation with “minimal information loss or distortion”
- Language Variety
- MT systems will be able to parse through and translate mixed-language content
- can be useful in English-Hindi, Ukrainian-Russian, Castilian-Latin American Spanish, European-Brazilian Portuguese, and other hybrid or dialectal language combinations
- Extended Context
- allows MT systems to assess “whether or not the context of the previous or next source sentences should influence the translation of a given sentence”
- MT systems will understand documents much better as a whole, allowing for better pronoun and noun agreement
- MT systems will translate certain words according to a given glossary, allowing for more conformity in the final result
These are but some of the numerous possibilities metadata opens MT systems up to. While not a tremendously revolutionary development, the application of metadata to MT solves “post-editing challenges in ways not possible in previous NMT generations,” slowly raising the bar for MT machine intelligence and capacity.
Arle Lommel, senior analyst at CSA Research—an independent market research firm—claims that the next major trends in machine translation will be the “shift to context-driven MT” and the “emergence of metadata-aware MT.” Having the proper metadata to account for nuances and important factors in translation will greatly improve the level of human parity in MT systems, Lommel argues. Lommel calls this kind of MT system “responsive machine translation,” as it can “respond intelligently to stakeholder requirements at multiple levels and deliver the best possible output for given contexts.” Apptek, in essence, is developing responsive machine translation.
The responsive machine translation model seems to be the logical stepping stone to further developments in MT. While not completely reformational, the responsive MT model drastically improves the current MT status quo with its context-driven translation at the segment and document level. And translating into languages where metadata is critical—Japanese with its honorifics, English with its varieties, Chinese with its dialects—will become all the easier, bridging the gap between cultures and people.
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