9

The source of this mistranslation series was identified by Japanese internet users as parallel corpus data contaminated by a Japanese TV reality show program あいのり Ainori. According to this article, a number of "episodes" other than "78" were observed as well. トナカイさんの贈り物 → Episode 167 星の印 → Episode 58 砂漠の決断 → Episode 60 恋する勇気 → Episode 50 愛の泉 → ...


4

This is due to one of the mayor disadventages of statistical machine translation systems as the one Google is using: In general nobody knows exactly based on which information a certain mistranslation was created on - so fixing is not a trivial thing. The algorithm just states: Based on hundreds of millions of lines of translated texts in my database A seems ...


3

One potential source of this kind of seemingly incomprehensible corpus-poisoning could be the use of translation memories from Computer-Aided Translation tools. Human translators not infrequently have to deal with badly formatted and segmented source texts, and in particular with texts where hard line breaks (etc.) have been used for layout purposes. This ...


3

Well, a word like 'bank' that is ambiguous between 'financial institution' and 'side of a river' will often be disambiguated by context. So, the first sense is talked about near words like 'money', while the second sense is talked about near words like 'water'. An SMT model will pick up on this regularity, and when it sees something like "money....bank", it'...


2

Look if the surrounding context has anything to do with Japanese pop-culture. The "花*花" duet had a song in 2012 titled "さよなら 大好きな人". "Take on Me" and "When the Going get Tough, the Tough Get Going" are also song titles.


1

Checkout the ACL their papers are (nearly?) all available online. Use Google Scholar to figure out which are the important ones ;)


1

Here are some. Hope that helps. 1) Hobbs, J. R. and Shieber, S. M. (1987). An algorithm for generating quantifier scopings. Computational Linguistics,13(1), 47–55. 2) Main, M.G. and Benson, D. B. (1983). Denotational semantics for natural language question-answering programs. American Journal of Computational Linguistics, 9(1), 11-21. 3) Simmons, R. F. (...


1

I don't think you've given enough detail about what you want to do with 'some statistical analysis', but I'll take a stab. I am not a stats expert, so these rough ideas will need to be checked by someone who is; I present them for you to know what to ask about. If you want to be able to say 'it's 85% likely that this person is from the longer VOT group', ...


1

There are several points here that I should address. What we have gotten about the expected per word entropy of random yet grammatical text is just some upper bound of the the expected per word entropy, because we have not found the exact way to compute the probability of words. I agree (roughly) with the first part of the sentence; we are getting closer ...


1

You could use hunalign or one of the tools used by the open source parallel corpus (the main one, uplug, has several parts designed to align sentences). I also read about CorporAl but I do not know if there is a stable version of it yet.


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