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Question Generation using Natural Language processing

#artificialintelligence

This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.


Question Generation using Natural Language processing

#artificialintelligence

Auto generate assessments in edtech like MCQs, True/False, Fill-in-the-blanks etc using state-of-the-art NLP techniques. This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use.


Reasoning-Driven Question-Answering for Natural Language Understanding

arXiv.org Artificial Intelligence

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.


Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

arXiv.org Artificial Intelligence

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.