### Research Takeaway : Author Outreach!

If you are the author of research for which I've written a 'takeaway' and you would like to comment or clarify - please let me know : I'd be very happy to learn how I can put things right.

This write-up contains my first impressions of the paper : A Scalable Hierarchical Distributed Language Model - (Mnih and Hinton 2009).

### Paper Background

The main aim of the paper was to reduce the output stage of a next-word prediction task from an O(V) representation (where V is the vocabulary size) to a tree-based representation that is O(log(V)).

This tree creation algorithm was a two-stage method : stage 1 involved operating on a random tree, so that some vector embedding could be performed, then stage 2 produced a tree based on the word vectors found in stage 1, and then produced the final word embeddings from there.

### Surprising stuff

• The paper reported that if words were allowed to appear multiple times in the tree, the training didn’t seem to pick up multiple senses : The duplicate entries favoured rare words, in similar settings, rather than common, multi-sense words

• The issues addressed in building trees (eg: how to reasonably divide up the samples at the node between left and right children) were those common in the tree recursive methods (e.g. CART) community - and it seemed like a less-than-in-depth treatment of an area that is actively researched

• Unlike some other papers, this one included generous attribution of other people’s ideas, and a clear exposition of the reasoning behind each choice at each decision point

• Hierarchical methods were plainly a computational win over the ‘full-V’ LBL model. However, it seemed like the authors had to ‘pull out all the stops’ to get perplexity performance to beat the complexity-competitive KN5 model

• Also interesting, for using Restricted Boltzman Machines (though not explained here why they’ve “moved on”, nor even mentioned, but to refer to previous benchmark results) : Three new graphical models for statistical language modelling - (Mnih and Hinton 2007)

### Ideas to Follow-Up

The paper Learning word embeddings efficiently with noise-contrastive estimation - (Mnih 2013) seems to supercede this approach (the tree-element of which is also taken up in Efficient Estimation of Word Representations in Vector Space - (Mikolov et al, 2013)). Will look at the former next.