Agglutinative Language
I often wonder how much of a head start the isolating nature of English gave for computing. It allowed ignoring a lot of inflectional and agglutinative complexity.<p>Concretely I mean it&#x27;s very easy to generate text using sentence templates. Just plug in words and it works out. &quot;The $process_name has completed running.&quot; &quot;Like $username&#x27;s comment&quot; &quot;Ban $username&quot;.<p>Relatedly, I think focusing NLP efforts on English masks a lot of interesting phenomena, because English text already comes in a reasonably tokenized, chunked up and pre-digested, easy to handle form. For example speech recognition systems started out with closed vocabularies, with larger and larger numbers of words, and even in their toy forms you could recognize some proper English sentences. To do that in Hungarian for example, the &quot;upfront costs&quot; to a &quot;somewhat usable&quot; system are much higher, because closed vocabulary doesn&#x27;t get you anywhere. (Similarly, learning basic English is very easy, you can build 100% correct sentences on day 1, you learn &quot;I&quot;, &quot;you&quot;, &quot;see&quot; and &quot;hear&quot; and can say &quot;I see&quot; and &quot;You see&quot; and &quot;I see you&quot; and &quot;I hear Peter&quot; which are all 100% correct. In Hungarian these are &quot;nézek&quot;, &quot;nézel&quot;, &quot;nézlek&quot;, &quot;hallom  Pétert&quot; requiring learning several suffixes and vowel harmony and definite&#x2F;indefinite conjugation. The learning curve till your first 100% correct 3-5 word sentences is just steeper.)<p>I don&#x27;t mean it&#x27;s impossible to handle agglutinative languages in NLP, I just mean the &quot;minimum viable model&quot; is much simpler and attainable for English, which on the one hand was able to kickstart and propel the early research phases and on the other hand perhaps fueled a bit too much optimism.<p>English can seem very well structured and it can tempt one to think of language in a very symbolic, within-the-box, rule-based way. In terms of syntax trees, sets of valid sentences etc, instead of &quot;fuzzy probabilistic mess&quot; that it really is. Surely, the syntax tree, generative grammar approach (Chomsky and others) gave us a lot of computer science, but this kind of &quot;clean&quot; and pure symbolic parsing doesn&#x27;t seem to drive today&#x27;s NLP progress.<p>In summary, I wonder how linguistics and especially computational linguistics and NLP would have evolved in a non-Anglo culture, e.g. Slavic or Hungarian.