Task: Language Identification
Input (Spanish, English): Review: Multimodal Word Distributions  - Strengths:  Overall a very strong paper.  - Weaknesses: The comparison against similar approaches could be extended.  - General Discussion:  The main focus of this paper is the introduction of a new model for learning multimodal word distributions formed from Gaussian mixtures for multiple word meanings. i. e. representing a word by a set of many Gaussian distributions.  The approach, extend the model introduced by Vilnis and McCallum (2014) which represented word as unimodal Gaussian distribution. By using a multimodal, the current approach attain the problem of polysemy.  Overall, a very strong paper, well structured and clear. The experimentation is correct and the qualitative analysis made in table 1 shows results as expected from the approach.  There’s not much that can be faulted and all my comments below are meant to help the paper gain additional clarity.   Some comments:   _ It may be interesting to include a brief explanation of the differences between the approach from Tian et al. 2014 and the current one. Both split single word representation into multiple prototypes by using a mixture model.   _ There are some missing citations that could me mentioned in related work as :  Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space Neelakantan, A., Shankar. J. Passos, A., McCallum. EMNLP 2014 Do Multi-Sense Embeddings Improve Natural Language Understanding? Li and Jurafsky, EMNLP 2015 Topical Word Embeddings. Liu Y., Liu Z., Chua T.,Sun M. AAAI 2015  _ Also, the inclusion of the result from those approaches in tables 3 and 4 could be interesting.   _ A question to the authors: What do you attribute the loss of performance of w2gm against w2g in the analysis of SWCS?  I have read the response.
Output (English): en