Question Answering Bot

Conversational Question Answering dataset released by Stanford NLP in 2019. It is a large-scale dataset for building Conversational Question Answering Systems. This dataset aims to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. The unique feature about this dataset is that each conversation is collected by pairing two crowd workers to chat about a passage in the form of questions and answers and hence, the questions are conversational. 

 It is one of the most popular and widely used NLP models. BERT models can consider the full context of a word by looking at the words that come before and after it, which is particularly useful for understanding the intent behind the query asked. Because of its bidirectionality, it has a deeper sense of language context and flow and hence, is used in a lot of NLP tasks nowadays. More details about BERT in the article along with the code.

What's new in MWS Conversational Question Answering bot

The Vision API can distinguish and extricate text from pictures. There are two explanation includes that help optical person acknowledgment (OCR):

    • TEXT_DETECTION recognizes and removes text from any picture. For instance, a photo could contain a road sign or traffic sign. The JSON incorporates the whole removed string, as well as individual words, and their jumping boxes.

    • DOCUMENT_TEXT_DETECTION additionally extricates text from a picture, yet the reaction is streamlined for thick text and reports. The JSON incorporates page, block, passage, word, and break data.


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