3 ms·

You're right, and I'm surprised (and concerned) that there haven't been any retractions or errata published for the paper cited. That said, the sentence-to-vec

You're right, and I'm surprised (and concerned) that there haven't been any retractions or errata published for the paper cited. That said, the sentence-to-vector and paragraph-to-vector models are obvious extensions of Mikolov's original word2vec architecture (the performance of which has been extensively verified by people all over and by a myriad of reimplementations), and I can attest that they've outperformed traditional text vectorization techniques (different bag of words parameterizations, largely) as features for every task I've evaluated them on (can't be too specific publicly, unfortunately). I guess what I'm saying is that ideas can be decent in spite of a poor first implementation.

lrei 10y ago
> I'm surprised (and concerned) that there haven't been any retractions or errata published for the paper cited there is a subsequent paper by Mikolov and Mesnil at ICLR with the correct results. And code to replicate them. Retraction? You want to retract a great paper because there is a 1-3% accuracy discrepancy for a result on a more or less random text classification task?
- pigscantfly 10y ago
  No, I think errata would be more appropriate; the gist of my comment was in support of the idea behind the paper, but it's disingenuous to pretend that the model works as well as described on that task. It's also disingenuous to trivialize the difference as 1-3% - that difference is more than a third of the actual error rate (7.42% vs. 11.27%) assuming you're referring to [1]. True, the IMDB dataset isn't a very important one, but it's important to clarify when you've made a mistake, especially if your paper has been cited hundreds of times. [1] https://arxiv.org/pdf/1412.5335v7.pdf https://arxiv.org/pdf/1412.5335v7.pdf