QA Quickstart

scenarios/README.md

@@ -10,6 +10,7 @@ The following summarizes each scenario of the best practice notebooks. Each scen
 |---| ------------------------ | -------------------------------------------- | ------------------- |
 |[Text Classification](scenarios/text_classification)      |Topic Classification|en, zh, ar|BERT|
 |[Named Entity Recognition](scenarios/named_entity_recognition) |Wikipedia NER                                              | en, zh  |BERT|
+|[Question Answering](scenarios/question_answering) |SQuAD                                              | en |BiDAF|
 |[Sentence Similarity](scenarios/sentence_similarity)      |STS Benchmark                         |en|Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance|
 |[Embeddings](scenarios/embeddings)| Custom Embeddings Training|en|Word2Vec<br>fastText<br>GloVe|
 

scenarios/question_answering/README.md

@@ -0,0 +1,16 @@
+# Question Answering (QA)
+
+This folder contains examples and best practices, written in Jupyter notebooks, for building question answering models. These models can be used in a wide variety of applications, such as search engines, and virtual assistants.
+
+## What is Question Answering?
+
+Question Answering is a classical NLP task which consists of determining the relevant "answer" (snippet of text out of a provided passage) that answers a user's "question". This task is a subset of Machine Comprehension, or measuring how well a machine comprehends a passage of text. The Stanford Question Answering Dataset ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)) leaderboard displays the state-of-the-art models in this space. Traditional QA models are varients of Bidirectional Recurrent Neural (BRNN) Networks. 
+
+## Summary
+
+The following summarizes each notebook for Question Answering. Each notebook provides more details and guiding in principles on building state of the art models.
+
+|Notebook|Runs Local|Description|
+|---|---|---|
+|[Deployed QA System in Under 20 minutes](question_answering_system_bidaf_quickstart.ipynb)| No| Learn how to deploy a QA system in under 20 minutes using Azure Container Instances (ACI) and a popular AllenNLP pre-trained model called BiDAF.
+