Machine translation meetup 2
Meetup organised by the Machine Translate Foundation
The second machine translation meetup took place online on 21 October, 2022.
A panel of guests discussed machine translation for low-resource languages.
- Francisco Guzman, Meta AI
- John E. Ortega, AmericasNLP
- Atul Kr. Ojha, LoResMT
- Ayushman Dash, NeuralSpace
- Idris Abdulmumin, Masakhane
The machine translation meetup was organised by the Machine Translate Foundation.
There was not enough time to answer all of the audience questions during the meetup. The guests kindly answered those questions in writing.
What causes a low-resource language to get on the radar of researchers, or to get launched in major products?
I think a number of factors might be at play:
- Number of speakers of the languages
- Availability of basic resources (online monolingual text, dictionaries, etc)
- Availability of professional translators to help create more data/evaluate
- Influence from advocacy groups
Are your approaches also applicable to historical / dead languages, which are mostly “very low-resource”?
Although it’s interesting to see historical languages as low-resource, I’m focusing my research on languages that would be most impactful to bridge language barriers of living people.
As such, historical languages are not very impactful.
Quality of translation depends on a myriad of factors, not only the amount of training data.
For example, whether there is a related language that you can co-train or not.
However, in No Languages Left Behind we found that a clean seed data of approximately 6 thousand sentence pairs was useful to bootstrap mining, backtranslation and training.
In machine translation, and natural language processing in general, quality is most associated with performance on some metrics, and seldom, on some form of human evaluation.
But this is most times constraint on the test set in consideration. The actual ‘quality’ of translation systems that don’t get the required attention in research is the ability of these systems to meet some decent performance after deployment, where actual users supply all kinds of unstructured, informal data for translation.
To achieve this, we need way more than the 6 thousand sentences that was suggested in No Languages Left Behind.
Generating translations of data is expensive and time consuming.
For benchmark data (like FLORES) crowdsourcing translation is not appropriate, as it lacks the quality process needed.
It’s possible that it is more appropriate for bilingual training data, which is OK if it’s noisy.
I think there is a lot of potential for monolingual data generation, which is not readily available in many low-resource languages.
Marcin from Microsoft says that a lot of low-resource machine translation is just taking high-res language datasets and making them smaller, but this doesn’t really reflect a real low-resource scenario. How are low-resource language datasets in reality?
I agree with this observation.
As a reviewer, I push back when people label “ablated” datasets, that is, smaller versions of a larger dataset, as low-resource.
Real low-resource languages are noisier, include code-switching, have different scripts, non standardized orthography (that is, same word can be spelled differently in the same dataset).
This is sadly true.
A lot of researchers work on these big datasets and then simulate low resource conditions on the high resource datasets just to generalize their findings.
Simulated low resource dataset usually consist of random text and, as a result, lacks the authenticity of document level texts.
Actual low resource data is more structured and also more restrictive in its coverage of the actual language in consideration while simulation just produces a lot of different texts.
Do you have any advice on useful approaches, tools and methods for creating parallel corpora from scratch?
Always check that you’re not paying human translation price for machine translation price.
That is, if you’re asking an language service provider to provide translations, verify that translators are not post-editing Google Translate, Microsoft Translator, Amazon Translate, or that the post-editing rates are clearly stated.
This is super important if you want to build a benchmark.
You don’t want to limit the research community to what the current translation engines are capable of.
I have seen situations where non-natives are paid to translate for a language just because they are from a country where that language is spoken, or where translators have not lived within the community for a long time.
It is worthwhile ensuring that translators should not only be speakers of the language but should live where the language is spoken.
Language changes with time.
What is the minimum investment in terms of training set size to make machine translation for a low-resource language usable?
With the availability of pre-trained language models and other supporting resources, it will be advisable to have at least 50 thousand to 100 thousand of qualitative and diverse human translations.