Again the irrelevant synonyms are potential distractors and incorrect answers. John H. Wolfe. For identifying whether the system has generated the questions corrected or not, I had taken help of manual evaluation as well as automatic evaluation to a certain extent. For training purpose, I had made use of Wiki articles as well in order to help my system to formulate better multiple choice questions and Wh- clause questions as discussed later in the sections. What will be the output of the following code snippet? If nothing happens, download the GitHub extension for Visual Studio and try again. For instance if the target keyword selected is classified as Person entity then the clause to be used more aptly is “Who” rather then “When” or “How”. Select topically important sentences using Noun, pronoun, verb, adverb into consideration. In couple of papers the overall process for generation automatic question for the English language is described as follows: (i) perform a morph syntactic, semantic and/or discourse analysis of the source sentence, (ii) identify topically important keywords from the sentances for question formulation, (iii) replace the topically important keyword with a blank or a adequate Wh question (iv) post-process the question and ensure it is grammatically correct [4,5]. 147-175. T5 is a new transformer model from Google that is trained in an end-to-end manner with text as input and modified text as output.You can read more about it here.. Most of the times the online texts do not come with review questions or practice assessments. Do try Quillionz for free. The antonyms generated are directly classified as the potential distractors. 2) If terms in the sentences have negative words like “could not / does not” convert to “could / does” and vice versa. Hence the words picked from random list was also a part of distractors and incorrect answers. Open a new file in a text editor or your Python IDE. Research paper, code implementation and pre-trained model are available to download on the Paperwithcode website link. Import the random library. There were various packages like NLTK and other natural language packages which were used along with my algorithms. To create a generator, you define a function as you normally would but use the yield statement instead of return, indicating to the interpreter that this function should be treated as an iterator:The yield statement pauses the function and saves the local state so that it can be resumed right where it left off.What happens when you call this function?Calling the function does not execute it. The good news is that you don't need to download each one of those individually . Disclaimer: This dissertation has been written by a student and is not an example of our professional work, which you can see examples of here. With help of NLTK dictionary and wordNet, I try to generate all possible antonyms and synonyms for the given word. 2) Manual intervention was needed but reduced compared to earlier approach. Figure 2: The fill in the blank question generation. You will need to install some packages below: 1. numpy 2. pandas 3. matplotlib 4. pillow 5. wordcloudThe numpy library is one of the most popular and helpful libraries that is used for handling multi-dimensional arrays and matrices. The original text from which the question in Figure [1] was generated is “Insider is someone with access right to the system”. Info: 8797 words (35 pages) Dissertation They made use of Thesaurus for getting distractors as well. This is shown in the Figure [11], where Chetan Bhagat is an author who wrote the book called Two states, so the first question which is formulated is acceptable but on other hand the second question generated does not seem to be acceptable. Figure 13: Multiple choice True/False result analysis. The votes for Definitely Yes compared to votes for remaining category are evenly distributed which implies to a certain extent that the system was able to perform well on evaluating the student’s performance on the knowledge of the topic. One of the issue is that, if the text is too technical in nature then generating synonyms and antonyms is difficult. There are graphical user interfaces which are again created in Python, with help of TkInter. Write a Python program to find the list of words that are longer than n from a given list of words. Copy. Many researchers have proposed some strategies for automatic question generation, most of them focused on vocabulary assessment only. The synonyms generated are passed through the usage checker module which internally calls Google api and classifies the synonyms are relevant and irrelevant. For the first application researchers have used techniques of generating questions and dialogues from expository texts [11, 12]. [Optional] Start two servers to speed up the script, Stanford Parser server and the SST servers in two separate terminals. This survey helped me to understand that probably more fine tuning of the question generation needs to be done but at the same time the questions created by the system are good enough to trick the user as well as gauge the concepts. I want to plot a knowledge graph from text paragraph as input in python, So wanted to know is there any transfer learning model, machine learning method or python library which helps in generating D. Lindberg, F. Popowich, J. Nesbit, and P. Winne, “Generating Natural Language Questions to Support Learning on-line”, In Proceedings of the 14th European Workshop on Natural Language Generation, 2013, pp. This framework is a rule based approach which has been trained on data as well with help of OpenNLP. One of the approaches to tackle this issue was discussed in  the paper having existing paraphrase generation techniques (Callison-Burch, 2007; Kok and Brockett, 2010). Interesting examples of these steps have also been discussed in Yao and Zhang [6] which made use of  Minimal Recursive Semantics. Extension of 2. A small snapshot of their rating is shown in the Table[2]. Made use of regex patterns to identify terms and numbers to manipulate the facts. The figure gives an idea about the overall system working where we load the data from the slides, books and pdf do some preprocessing of the data and generate the questions.