If the $\chi^2$ statistics are biased will depend on various issues that it is currently impossible from your question to infer.
In general, you want to have random sampling from a population and also a sufficient amount of frequencies for each cell (due to rounding errors in discrete values). The latter condition can be subjective regarding "what is sufficient", but simulations revealed that $>5$ for each cell is a good rule of thumb. The random sampling ensures that independent observation will have a mean that is approximately normally distributed which is a prerequisite for calculation of the $\chi^2$ test statistics. All this provides a good set up for obtaining unbiased $\chi^2$ statistics.
Now comes the tricky part: You need to ensure that each cell is mutually exclusive of each other and that you cover ALL categories (exhaustively). It is not entirely clear from your question if that is the case for the variable grammatical structure or language. For example, Italian, German, Japanese are all languages, but do you have a categories for the rest, so to speak "all other languages" as well? Furthermore, is are the grammar categories mutually exclusive? I am no linguist but I can imagine that sometimes there is no clear cut here. Also what are your actual observations here, where does data for your study originate from?
Given that you have taken care of all this, $\chi^2$ ought to be be relatively unbiased. And do not worry, the dimension of the p statistics (so close to zero) you have shown is quite frequent in $\chi^2$ tests with large data sets - even when you have very small differences between expected and actual value. This is because the test becomes very accurate in large samples, and you can confidently claim, for example, 10 is (statistically) smaller than 10.01. A large sample quickly shoots up the $\chi^2$ statistics even for small differences in expected and actual values, which is the nature of the test and not a bias by itself.
So there is a good chance that your test statistics are actually right, but as I said, you need to make sure that your categories are aptly chosen, that is they are mutually exclusive, exhaustive and best if evenly frequent. Furthermore, for your observations you must ensure a random sampling from the population. (Of course, if you don't have a sample but you take the whole population into account, you won't need a $\chi^2$ test in the first place.)