ParaQG: A System for Generating Questions and Answers from Paragraphs (original) (raw)

Automatic Question Generation: A Syntactical Approach to the Sentence-To-Question Generation Case

2012

Humans are not often very skilled in asking good questions because of their inconsistent mind in certain situations. Thus, Question Generation (QG) and Question Answering (QA) became the two major challenges for the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System, and Information Retrieval (IR) communities, recently. In this thesis, we consider a form of Sentence-to-Question generation task where given a sentence as input, the QG system would generate a set of questions for which the sentence contains, implies, or needs answers. Since the given sentence may be a complex sentence, our system generates elementary sentences from the input complex sentences using a syntactic parser. A Part of Speech (POS) tagger and a Named Entity Recognizer (NER) are used to encode necessary information. Based on the subject, verb, object and preposition information, sentences are classified in order to determine the type of questions to be generated. We conduct extensive experiments on the TREC-2007 (Question Answering Track) dataset. The scenario for the main task in the TREC-2007 QA track was that an adult, native speaker of English is looking for information about a target of interest. Using the given target, we filter out the important sentences from the large sentence pool and generate possible questions from them. Once we generate all the questions from the sentences, we perform a recall-based evaluation. That is, we count the overlap of our system generated questions with the given questions in the TREC dataset. For a topic, we get a recall 1.0 if all the given TREC questions are

Automation of Question Generation From Sentences

… of QG2010: The Third Workshop on …, 2010

Abstract. Question Generation (QG) and Question Answering (QA) are key challenges facing systems that interact with natural languages. The potential benefits of using automated systems to generate questions helps reduce the dependency on humans to generate ...

Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation

2020

Question generation (QG) has recently attracted considerable attention. Most of the current neural models take as input only one or two sentences, and perform poorly when multiple sentences or complete paragraphs are given as input. However, in real-world scenarios it is very important to be able to generate high-quality questions from complete paragraphs. In this paper, we present a simple yet effective technique for answer-aware question generation from paragraphs. We augment a basic sequence-to-sequence QG model with dynamic, paragraph-specific dictionary and copy attention that is persistent across the corpus, without requiring features generated by sophisticated NLP pipelines or handcrafted rules. Our evaluation on SQuAD shows that our model significantly outperforms current state-of-the-art systems in question generation from paragraphs in both automatic and human evaluation. We achieve a 6-point improvement over the best system on BLEU-4, from 16.38 to 22.62.

AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 2021

One strategy for facilitating reading comprehension is to present information in a questionand-answer format. We demo a system that integrates the tasks of question answering (QA) and question generation (QG) in order to produce Q&A items that convey the content of multi-paragraph documents. We report some experiments for QA and QG that yield improvements on both tasks, and assess how they interact to produce a list of Q&A items for a text. The demo is accessible at qna.sdl.com.

Automated Question Generator System: A Review

International Journal of Engineering Applied Sciences and Technology

Question generation, the task of automatically creating questions that can be answered by a certain span of text within a given passage, is important for questionanswering and conversational system in digital assistants. Automatic generation of questions from text plays a key role in two domains-interactive question answering sessions and educational assessment. Recent sequence to sequence neural models have outperformed previous rulebased system. Existing models mainly focus on using one or two sentences as the input. In proposed system the admin can add text or paragraphs of his/her choice. User will operate that system hence user can enter the paragraph in English language with grammatically correct sentence. The sentence is selected and separated and Stanford POS tagger for POS Tagging is applied. After the input is given, keywords from a data-set are matched to the input text so as to find the sentence/context on which the question can be created. In feature extraction the system will identify the questionable term from that sentence and rearrange the words and automatically create question from entered sentence or paragraph using Bloom's Taxonomy. However, it often requires the whole paragraph as context in order to generate high quality questions. Proposed system uses Stanford tagger for tagging the sentences with gated self-attention encoder to address the challenges of processing long text input for question generation. With sentence-level input, this model outperforms previous approaches with either sentence or paragraph input. Furthermore proposed model can also effectively utilize paragraphs as inputs.

Question generation from paragraphs at UPenn: QGSTEC system description

This paper describes the question generation system devel-oped at UPenn for QGSTEC, 2010. The system uses predicate argument structures of sentences along with semantic roles for the question gener-ation task from paragraphs. The semantic role labels are used to identify relevant parts of text before forming questions over them. The generated questions are then ranked to pick final six best questions.

Automatic Question Generation and Evaluation

2021

Generation of questions from an extract is a very tedious task for humans and an even tougher one for machines. In Automatic Question Generation (AQG), it is extremely important to examine the ways in which this can be achieved with sufficient levels of accuracy and efficiency. The ways in which this can be taken ahead is by using the Natural Language Processing (NLP) to process the input and to work with it for AQG. Using NLP with question generation algorithms the system can generate the questions for better understanding of the text document.The input is pre-processed before actually moving in for the question generation process. The questions formed are first checked for proper context satisfaction with the context of the input to avoid invalid or unanswerable question generation. It is then preprocessed using various NLP based mechanisms like tokenization, named entity recognition(NER) tagging, parts of speech(POS) tagging, etc. The question generation system consists of machin...

Automatic Question-Answer Pairs Generation from Text

Possible opportunities for question-answer generation have been suggested in the previous work, including in the field of education. The need of questions and answers is prompted for various purposes, e.g. self-study, academic assessment, and coursework. However, the conventional way to create question-answer pairs has been both tedious and time-consuming. In the present study, we propose an automatic question generation for sentences from text passages in reading comprehension. We introduce a rule-based automatic question generation for the task, as well as implement statistical sentence selection and various configurations of named entity recognition. Three types of WH-questions (" What " , " Who " , and " Where ") can be produced by our system. The system performs well on generating questions from simple sentences, but falters on more complex sentences due to incomplete transformation rules.

Question Generation: Past, Present & Future

2024

Question Generation (QG) is an essential area in Natural Language Processing (NLP) that aims to create questions from a given text. This paper reviews the evolution of QG methods, from early rule-based systems to contemporary deep learning techniques, and explores potential future advancements. By examining the strengths and weaknesses of each approach, we provide a comprehensive understanding of the progress in QG and propose directions for future research.

Automatic Question Generation: A Systematic Review

SSRN Electronic Journal, 2019

Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers while getting questions from external sources like assessment books or question bank might not be relevant to content studied by students. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content, which can be text. Automatic question generation (QG) is a very important yet challenging problem in NLP. It is defined as the task of generating syntactically sound, semantically correct and relevant questions from several input formats like text, a structured database or a knowledge base. Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. AQG has the got the immense attention from researchers in a field of computational linguistics. The review paper focuses on the recants ongoing research on NLP for generating automatic questions from the text through various methods. http://ssrn.com/link/ICAESMT-2019.html=xyz Information Systems &eBusiness Network (ISN) Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. Despite its usefulness, manually creating meaningful and relevant questions is a timeconsuming and challenging task. For example, while evaluating students on reading comprehension, it is tedious for a teacher to manually create questions, find answers to those questions, and thereafter evaluate answers. Traditional approaches have either used a linguistically motivated set of transformation rules for transforming a given sentence into a question or a set of manually created templates with slot fillers to generate questions. Recently, neural network-based techniques such as sequence-to-sequence (Seq2Seq) learning have achieved remarkable success in various NLP tasks, including question generation. A modern approach that is deep learning is used to generates question. The approach proposed by author explore a straightforward task of question generation only from a triplet of subject, relation and object. Sequence-to-sequence prototype with attention for question generation from passages. The proposed algorithm generates questions and answers from corpus using pointer networks.