Paper:
Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of n-gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par F 1 scores while decreasing the time and memory requirements by several times compared to the conventional n-gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, the memory reduction was about 100 times and train and test speed-ups were over 100 times. More importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting the strict dependency between the dimensionality of the representation and the parameters of n-gram statistics, thus, opening a room for tradeoffs.

Review:
This paper proposes the use of hyperdimensional (HD) vectors to represent n-gram statistics. The HD vectors are first generated from the whole corpus. Then, it is aggregated or bundled to a vector for each sample as an input of a classifier training. The evaluation is conducted on four datasets: Chatbot, AskUbuntu, WebApplication and 20 News Group using a bunch of classifier including KNN, Random Forest, MLP etc.
It is interesting to see how to hash/project the high dimensional n-gram vector into a lower space for efficiency. The approach is useful in online production systems, and it is eco-friendly. However, there are a few concerns detailed as follows:
1. Can it be generalized to contemporary learned embeddings, e.g., word2vec and GloVe? 
2. Lack of proper baselines for comparison. Word2vec, GloVe are trained on large corpora once and can be applied directly to other tasks, and they should be served as baselines. Furthermore, the simple bag of word/TF-IDF should be included as baselines as well.
3. Lack of analysis: it is hard to understand what kind of HD vectors are generated. Are these n-grams semantically related projected nearby in the HD space? This helps readers to understand the constructed embeddings.
4. The SentEval benchmark is popular in sentence level representation learning and it is well known. It is better to see some evaluations on it as well. http://www.lrec-conf.org/proceedings/lrec2018/pdf/757.pdf
Minor comments: 
1. The Subword Semantic Hashing is originally from DSSM published in 2013.  (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) 
2. What is $v_c$ in 4.2?