[gojomo]

While I still need to read this paper in detail, I'm not sure their only change is to this scaling of the update.

The `koan` CBOW change has mixed effects on benchmarks, and makes their implementation no longer match the choices of the original, canonical `word2vec.c` release from the original Google authors of the word2vec paper. (Or, by my understanding, the CBOW mode of the FastText code.)

So all the reasoning in that issue for why Gensim didn't want to make any change stands. Of course, if there's an alternate mode that offers proven benefits, it'd be a welcome suggestion/addition. (At this point, it's possible that simply using the `cbow_mean=0` sum-rather-than-average mode, or a different starting `alpha`, matches any claimed benefits of koan_CBOW.)

[mlthoughts2018]

The paper itself says the only change is normalizing by the context window size C.

[gojomo]

Ah, but I've now looked at their code, and it's not the only change! They've also eliminated the `reduced_window` method of weighting-by-distance that's present in `word2vec.c`, Gensim, and FastText.

What if that's the real reason for their sometimes slightly-better, sometimes slightly-worse performance on some benchmarks? Perhaps there are other changes, too.

This is why I continue to think Gensim's policy of matching the reference implementations from the original authors, at least by default, is usually the best policy – rather than using an alternate interpretation of the often-underspecified papers.