anchor.diff:  So why hs DL been more successful in ASR and vision than NLP/NLU? That's a topic for another post! 
   
   
  -
  -[1] 
  -[2] http://arxiv.org/pdf/1606.01933v1.pdf [A Decomposable Attention Model for Natural Language Inference] 
  -[2]
  -[3]
  -[4]
  -[5] Karpathy
  -[6] Word2Vec
  -[7] Glove
  -[8] Google Translate
  +**References**
  +[1] https://arxiv.org/pdf/1607.01759v2.pdf[Bag of Tricks for Efficient Text Classification], A. Joulin, E. Grave, P. Bojanowski, T. Mikolov 
  +[2] https://github.com/facebookresearch/fastText[Facebook's fastText on Github]
  +[3] http://arxiv.org/pdf/1606.01933v1.pdf[A Decomposable Attention Model for Natural Language Inference] 
  +[4] http://arxiv.org/abs/1608.04207v1[Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks], Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg
  +[5]
  +[6] Karpathy
  +[7] Word2Vec
  +[8] Glove
   [9] Smart Reply
   [10] http://www.foldl.me/2016/solving-language/
   
  
positive.code: = Natural language modeling: How deep is too deep?
  :hp-tags: Deep Learning, NLP
  
  [.lead] Is Deep Learning always the best tool for Natural Language Understanding tasks? Not necessarily!
  
  Recently, a new paper from Google research appeared on arXiv [1], immediately catching the attention of the NLP community. Focusing 
  on the problem of Natural Language Inference (NLI), the paper made a convincing case for the overall superiority of ... *shallow* 
  sentence representations - as opposed to deep ones - for NLU tasks such as NLI. Furthermore, this paper is but one example in 
  the recent slew of results casting the efficacy of Deep NN architectures into question for text-related tasks. Not the least of which was 
  FAIR's FastText[2] (essentially a simple modification of the unsupervised Word2Vec algorithm to deal with supervised learning tasks). 
  See also  here[3] and here[4]. 
  
  Not familiar with NLI and why it is an important litmus test? Fear not: this paragraph is about to fill you in! 
  (For reasers familiar with the terminology, feel free to skip to the next paragraph). 
  <------->NLI EXPLANATION HERE <--------->
  
  Hold on, you might say, isn't Deep Learning a new disruptive force in AI, shown beyond doubt to be clearly superior to prior "shallow"
  learning approaches? Well, it depends who you ask. Ask a Computer Vision or a Speech Recognition guy, and you'll get and enthusiastic Yes!
  In (computer) vision, novel DL archirectures (such as VGG, GoogleNet, Inception, etc.) have delivered extremely impressive 
  results on ImageNet, CIFAR, even defying expectations of some DL champions in vision [5]. In ASR, commercial heaviweights such as 
  Google and Baidu have longs since switch to DL architectures. It should only be natural to expect, then, to see the 
  same trend in NLP/NLU, correct? 
  Well, not so fast. 
  
  The tide of enthusiasm in Deep Learning has of course spilled over to the NLU community, triggering a massive conversion of both 
  academics and industry practitioners to the newfound DL religion. Impressive results from other fields, 
  helped by the success of the seminal Word2Vec[6] (followed by Glove[7] and the like) was too much to resist. 
  RNN and LSTM have since become mainstream techniques, offered by 
  popular DL libraries such as TensorFlow, Keras, DL4J, etc. Among other big companies, Google has been at the forefront of both 
  migrating to DL for text machine learning tasks (such as translation[8], SmartReply[9], etc.)
  
  So, you ask? Isn't that enough? Well, dear friend: if you are reading this, chances are, you are not Google! And as a result, you 
  don't have the same massive amounts of training data, nor their virtually unlimited computational resources. To the rest of us, 
  it is important to understand the performance/computation tradeoffs that come with DL  -  which is what this post is really about. 
  
  So let us look at some key problems one by one. 
  
  *** Subsection: Тext Classification ***
  Common task with plenty of applications: search, intent classification, sentiment analysis, topic modeling 
  (slightly different, but close), seqeuence labeling (related), etc. 
  For text classification, we typically have 3 options: 
  A) Logit or RF with nag of words or averaged word vectors.  
  B) Shallow neural networks such as FastText
  C) Deep networks such as RNNs/LSTMs or CNNs. 
  
  
negative.code: 
  
  
  
  
  
  
  === NLI and machine translation. 
  Why is NLI important ? It's a key problem in natural language understanding. Many other NLU problems can be reduced to NLI: such as summarization 
  (given a piece of text and a suggested summary, does the former entail the latter), information extraction (does the text entail the extracted fact), 
  question answering (does the data source entail a given question and answer pair) as well as machine machine translation 
  