Associative Texture Is Lost In Translation

We present a suggestive finding regarding the loss of associative texture in the process of machine translation, using comparisons between (a) original and backtranslated texts, (b) reference and system translations, and (c) better and worse MT systems. We represent the amount of association in a text using word association profile – a distribution of pointwise mutual information between all pairs of content word types in a text. We use the average of the distribution, which we term lexical tightness, as a single measure of the amount of association in a text. We show that the lexical tightness of humancomposed texts is higher than that of the machine translated materials; human references are tighter than machine translations, and better MT systems produce lexically tighter translations. While the phenomenon of the loss of associative texture has been theoretically predicted by translation scholars, we present a measure capable of quantifying the extent of this phenomenon.