Reviews: Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images

The paper was clear and well written. The data set and the evaluation that was conducted could be useful to the community. However, the paper unfairly characterizes or omits some previous work, and was not clear enough about the limitations and biases of their evaluation strategy. These points detract from a paper that otherwise makes an interesting contribution. First, there is an implied criticism of WordSim-353 and MEN at the bottom of page 2 that they only contain similarity judgments at the word level. However, there is a large amount of work on learning phrase and sentence-level embeddings in the recently literature that overcome these issues (see representative work by Mirella Lapata, Marco Baroni, Stephen Clarke, Richard Socher, among many others), which the paper does not mention.