Task: Language Identification
Input (Spanish, English): - Strengths: This paper presents an extension of many popular methods for learning vector representations of text.  The original methods, such as skip-gram with negative sampling, Glove, or other PMI based approaches currently use word cooccurrence statistics, but all of those approaches could be extended to n-gram based statistics.  N-gram based statistics would increase the complexity of every algorithm because both the vocabulary of the embeddings and the context space would be many times larger.  This paper presents a method to learn embeddings for ngrams with ngram context, and efficiently computes these embeddings.  On similarity and analogy tasks, they present strong results.  - Weaknesses: I would have loved to see some experiments on real tasks where these embeddings are used as input beyond the experiments presented in the paper.  That would have made the paper far stronger.  - General Discussion: Even with the aforementioned weakness, I think this is a nice paper to have at ACL.  I have read the author response.
Output (English): en