Tuesday, January 10, 2017

Some thoughts on Mikolov et al. 2013

I definitely find it as interesting as M&al2013 do that some morphosyntactic relationships (e.g., past tense vs. present tense) are captured by these distributed vector representations of words, in addition to the semantic relationships. That said, this paper left me desperately wanting to know why these vector representations worked that way. Was there anything interpretable in the encodings themselves? (This is one reason why current research into explaining neural network results is so attractive — it’s nice to see cool results, but we want to know what the explanation is for those results.) Put simply, I can see that forcing a neural network to learn from big data in an unsupervised way yields these implicit relationships in the word encodings. (Yay! Very cool.) But tell me more about why the encodings look the way they do so we better understand this representation of meaning.

Other thoughts:

(1) Everything clearly rides on how the word vectors are created (“…where similar words are likely to have similar vectors”). And that’s accomplished via an RNN language model very briefly sketched in Figure 1. I think it would be useful to better understand what we can of this, since this is the force that’s compressing the big data into helpful word vectors. 

One example:  the model is “…trained with back-propagation to maximize the data log-likelihood under the model…training such a purely lexical model to maximize likelihood will induce word representations…”  — What exactly are the data? Utterances?  Is there some sense of trying to predict the next word the way previous models did? Otherwise, if everything’s just treated as a bag of words presumably, how would that help regularize word representations?

(2) Table 2: Since the RNN-1600 does the best, it would be handy to know what the “several systems” were that comprised it. That said, there seems to be an interesting difference in performance between adjectives and nouns on one hand (at best, 23-29% correct) and verbs on the other (at best, 62%), especially for the RNN versions. Why might that be? The only verb relation was the past vs present tense…were there subsets of noun or adjective relations with differing performance, or were all the noun and all the adjective relations equal? (That is, is this effectively a sampling error, and if we tested more verb relations, we’d find more varied performance?) Also, it’d be interesting to dig into the individual results and see if there were particular word types the RNN representations were especially good or bad at. 

(3) Table 3: Since the RNN-1600 was by far the best of the RNNs in Table 2 (and in fact RNN-80 was the worst), why pick the RNN-80 to compare against the other models (CW, HLBL)?

(4) Table 4, semantic relation results: When .275 is the best Spearman’s rho can you can get, it shows this is a pretty hard task…I wonder what human performance would be. I assume close to 1.00 if these are the simple analogy-style questions? (Side note: MaxDiff is apparently this, and is another way of dealing with scoring relational data.)


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  2. Really enjoyed the article, and you are that is indeed max diff. One post that helped with max diff was https://www.displayr.com/how-max-diff-analysis-works/