machine reading comprehension tutorial

The final output of an encoder is a concatenation of the outputs of all blocks, as illustrated in the next figure.In the attention layer, we develop a two-step attention that folds in all information observed so far for generating final sequences. The result is “Tokyo.”In order for ELMo-BiDAF to work the way we want, it needs to be able to parse the semantic meaning of our question, and then find a suitable answer from the passage we give it. In the last post of this series, I have introduced the task of machine reading comprehension (MRC) and presented a simple neural architecture for tackling such task. by replacing interrogative, thus metrics based on literally matching may underestimate the quality of generated question. The effectiveness of our dual-learning model provides a strong evidence for leveraging the duality to improve the reading comprehension ability.In the real world, finding the best answer may require reasoning across multiple evidence snippets and involve a series of cognitive processes such as inferring and summarizing. During testing, the shifted input is replaced by the model’s own generated words from the previous steps.The embedding layer maps each word to a high-dimensional vector space.

I also pointed out an assumption made in this architecture: the answer is always a continuous span of a given passage. The vector representation includes the word-level and the character-level information. That being said, those metrics still serve as an inexpensive and scalable way to demonstrate the fluency of relevance of generated questions and answers from our model.Interested readers are encouraged to checkout the experiment section In this series, I have introduced the task of machine reading comprehension, a simple framework based on the span assumption, and a unified model that solves QA and QG simultaneously. Improving Machine Reading Comprehension with General Reading Strategies Kai Sun1 Dian Yu2 Dong Yu2 Claire Cardie1 1Cornell University, Ithaca, NY, USA 2Tencent AI Lab, Bellevue, WA, USA ks985@cornell.edu, fyudian, dyug@tencent.com cardie@cs.cornell.edu Abstract Reading strategies have been shown to im-prove comprehension levels, especially for readers lacking adequate prior … This tutorial is designed to illustrate that point by walking you through a simple, straightforward process for deploying a reading comprehension model.The ML library we’ll be using —AllenNLP—is used by Facebook, Airbnb, Amazon, and Before you begin, you should decide what you’ll be building, and what text it will need access to. This greatly reduces the solution space and simplifies the training, yielding This post is the part II of the Machine Reading Comprehension series. Note that, all models mentioned in this series can not even answer simple yes/no questions, e.g. For more generated examples, please As both question and answer are generated in our model, we adopt In general, automatically evaluating the quality of generated text is a difficult task, as two sentences have different words can have very similar meaning. If you’re building a chatbot, it will probably be more along the lines of Allganize’s. Instead, we’re going to use In order for our deployment to work, we need to give Cortex a configuration file, which will act as a blueprint for describing how our deployment should work. I will include screenshots of the chatbot frontend, but we will not be diving into code. The parameters of this layer are shared by context, question and answer. Under this assumption, an answer can be simplified as a pair of two integers, representing its start and end position in the passage respectively. Our model exploits the duality of QA and QG in two places.As every component in the proposed model is differentiable, all parameters could be trained via back propagation.In the first two samples, our dual model works perfectly, whereas the mono-learning model fail to provide the desired output.

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