Architecture question

[mtourne]

Pretty cool stuff. Reading the code I'm just wondering about why so many levels of indirection from indexes to word2vec sentence matrixes.

It's like parsing -> creation of an "alphabet" to map words to indexes -> creation of questions / answers as series of alphabet indexes -> creation of an alphabet index to word2vec mapping. This also requiring a nn layer that will do the lookup index to word2vec vector, before the convolution.

Is there a reason to bother with indexes at all, and not transforming everything straight into a word2vec matrix either at parsing time or even before the feed forward phase ? Seems like this way the code would be more tolerant to being fed new document pairs containing words that exist in the word2vec but not in the "alphabet" mapping.