Why Machine Translation Struggles with Philippine Languages

Machine translation learns from parallel text — the same content in two languages, aligned. For English and French there are decades of it: parliamentary records, treaties, subtitles, technical manuals. For Ilocano and Tagalog there is very little, and for Kapampangan or Pangasinan there is less still.

The Philippines has over a hundred languages. Tagalog has a national broadcasting industry behind it. Ilocano, with roughly 8.7 million people identifying as Ilocano in the 2020 census, has a fraction of that written output — and almost none of it paired sentence-for-sentence with another language.

This is what “low-resource” means in practice. It is not that the language is simple or that nobody has tried. There is simply not enough aligned text for the usual methods to work well.

Co-official in the Philippines and the bridge language most machine translation systems pivot through — including this one. Ilocano↔Tagalog output is usually routed via English internally, which is a large part of why it drops nuance.

Because there is so little direct Ilocano–Tagalog data, systems translate Ilocano into English, then English into Tagalog. Two translations happen where you asked for one, and each is a chance to lose something.

The failure this produces is distinctive. English is full of ambiguous words, and when the pivot lands on the wrong sense, the output is confidently and completely wrong:

Kannawanis the clearest case. It means “right” as in the direction. Routed through the English word “right”, it returns as Gamit ang karapatan— “using the legal right”. Nothing in the Ilocano suggests entitlement. English introduced it.

The pivot also destroys distinctions English does not make. Both Ilocano and Tagalog separate an inclusive “we” from an exclusive one. English has one word for both, so a distinction present in the source and available in the target is lost in the middle.

A translator that fails visibly is manageable. The problem with Philippine-language machine translation is that it usually fails invisibly.

When the engine does not recognise a word, it returns it unchanged. Nothing marks it as untranslated. In our audit, ten phrases came back identical to the input despite having ordinary equivalents in the target language — including Haan (no), Danom (water) and Ikan (fish).

What makes this genuinely hard to spot is that unchanged output is sometimes correct. Seventeen further phrases in the same test were identical in both languages legitimately — lima, pito, itlog, kape. So “it came back the same” tells you something is worth checking, not that something is broken.

Rather than assert any of this, we tested it. All 153translatable entries in our Ilocano–Tagalog phrasebook went through this site's own translator and were compared against the human-checked Tagalog.

Machine output matched or came close for 57% of them. Reliability tracked how concrete the vocabulary was: colours scored 86% and time expressions 77%, while family terms, introductions and everyday courtesy all came in at 33–44%.

That pattern makes sense. Colour and time words are short, common, and have one-to-one equivalents. Kinship and politeness carry social meaning that differs between cultures and does not survive a round trip through English.

Full results, including every failure, are on the accuracy page, and the method is described on the methodology page.

The category breakdown is the most useful thing to come out of the measurement, because it is predictive. You can tell in advance whether a phrase is likely to survive the trip.

The gradient runs from physical description down to social relationship. A colour is a colour in any culture. A term of address encodes who outranks whom, how well you know them, and how formal the setting is — and none of that survives a round trip through a language that marks it differently.

The failures in that bottom band are rarely nonsense. They are usually the right idea at the wrong level of formality, which is harder to catch and often more damaging.

Notice that Agyamanak gained a po that was never in the source. The engine added deference on its own. In the other direction it stripped warmth from Tatang, returning the formal Ama. Neither is a mistranslation in the dictionary sense; both change the relationship between speaker and listener.

In Philippine languages that is not a stylistic nicety. Getting formality wrong with an elder is a social error, and it is precisely the error a translation tool is least able to warn you about.

Nothing here is a permanent property of the languages. It is a data problem, and data problems are solvable — slowly.

• Direct parallel corpora. Ilocano and Tagalog text aligned sentence by sentence, removing the English pivot. This is the single highest-value fix and the hardest to produce.
• Honest confidence signals.The engine knows when it has failed to recognise a word — it returns the input unchanged. Surfacing that as “not translated” instead of presenting it as output would eliminate the most damaging failure mode overnight.
• Dialect awareness. Regional Ilocano vocabulary is passed through silently rather than flagged as unrecognised.
• Curated phrase data. The least glamorous option and the one available today: for common situations, a human-checked phrasebook outperforms any engine, because a person decided what the phrase means in context.

We have not measured Cebuano, Hiligaynon, Waray, Bikol, Kapampangan or Pangasinan, so we will not put a number on them. But the structural causes — thin parallel data, English as the pivot, silent passthrough — apply to all of them, and several have less available text than Ilocano.

Kapampangan is a particular case. It belongs to the Central Luzon branch rather than being closely related to Tagalog, despite the provinces bordering each other, so geographic proximity is no guide to translation quality.

A number is only useful if you know what it excludes. Ours has four limitations worth stating plainly, and two of them cut against us.

• The grading is automatic. A correct translation worded differently from ours is marked down. The true figure is probably somewhat better than 56.9%.
• The test set is short phrases. Our phrasebook is everyday expressions, which is where the engine struggles most because there is no context to disambiguate. Longer prose likely scores higher.
• One direction only. We measured Ilocano into Tagalog. The reverse has not been audited and we do not assume it behaves the same way.
• One engine, one point in time. Translation services change without notice. The figure describes what we observed on the date shown, not a permanent property.

We publish the number anyway, with the caveats attached, because the alternative — asserting that results are “fast and accurate” and offering nothing to check — is what every competing site already does.

There is an unfashionable conclusion buried in all of this. For the vocabulary people actually need most — greetings, courtesy, family, food — a curated phrase list compiled by people outperforms a general-purpose engine, and does so by a wide margin.

The reason is not that the engine is badly built. It is that these are precisely the phrases whose meaning is fixed by convention rather than composition. Agyamanakmeans thank you because that is what Ilocano speakers say, not because “I am grateful” decomposes that way. An engine translating the parts gets the parts right and the phrase wrong.

A human compiler settles it once, records the result, and everyone after that gets the right answer. That is why our 158 checked entries sit alongside the translator rather than behind it — for anything in that list, the list is the better answer.

Machine translation earns its place on everything else: the sentence nobody anticipated, the message that has to be understood roughly and now. Knowing which situation you are in is most of the skill.

• Translate whole sentences, not single words. Context is the main defence against the pivot problem.
• Treat unchanged output as a prompt to check, not as proof of either success or failure.
• Watch the length. Output noticeably longer than the input often means something was invented — our engine turned a two-clause greeting into one containing the word “guys”.
• Use checked phrase data where it exists. Our phrasebook covers common situations and was compiled by people.
• For anything legal, medical, official or public, hire a translator. A tool that is right four times in seven is a draft, not a document.