automatic question generation from paragraph

(2018) proposed to augment each word with linguistic features and encode the most relevant pivotal answer in the text while generating questions. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. In reality, however, it often requires the whole paragraph as … for question generation from text. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Research paper, code implementation and pre-trained model are available to download on the Paperwithcode website link. We propose a general hierarchical architecture for better paragraph representation at the level of words and sentences. 56–64 (2011) Google Scholar Also LSTM models are slower to train. Here the questions are generated only on the sentences selected in the previous module. In case if the purpose of your research is about language testing, you need to determine what question type you want to generate at first; e.g. To statistical ranking for question generation. No matter what essay topic you have been given, our essay generator will be able to complete your essay without any hassle. Majumder, and Li Deng. In the case of the transformer, the sentence representation is combined with its positional embedding to take the ordering of the paragraph sentences into account. Equipped with different enhancements such as the attention, copy and coverage mechanisms, RNN-based models (Du et al., 2017; Kumar et al., 2018; Song et al., 2018) achieve good results on sentence-level question generation. We also present attention mechanisms for dynamically incorporating contextual information in the hierarchical paragraph encoders and experimentally validate their effectiveness. This demo only uses the grammar to generate questions starting with 'what'. Question Generation In this module the actual work of generating questions takes place. We present human evaluation results in Table 3 and Table 4 respectively. In Arikiturri [4], they use a corpus of words and then choose the most relevant words in a given passage to ask questions from. Our first attempt is indeed a hierarchical BiLSTM-based paragraph encoder ( HPE ), wherein, the hierarchy comprises the word-level encoder that feeds its encoding to the sentence-level encoder. A few years ago we were wondering - is there a good paraphrasing website with an automatic paraphrasing tool online? Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs. See my Quora answers to: * Can computers make questions? Introduction paragraph generator picks your content and changes lots … 2018. Encoder-decoder attention layer of decoder takes the key Kencdec and value Vencdec . Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. Question Generation from Paragraphs: A Tale of Two Hierarchical Models Vishwajeet Kumar, Raktim Chaki, Sai Teja Talluri, Ganesh Ramakrishnan, Yuan-Fang Li, Gholamreza Haffari (Submitted on 8 Nov 2019) Automatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs. (2018) recently proposed a Seq2Seq model for paragraph-level question generation, where they employ a maxout pointer mechanism with a gated self-attention encoder. Automating reading comprehension by generating question and answer This Automatic Gap-Fill Question Generation system creates multiple choice, fill-in-the-blank questions from text corpora. Yes. Jeffrey Pennington, Richard Socher, and Christopher Manning. Our split is same but our dataset also contains (para, question) tuples whose answers are not a subspan of the paragraph, thus making our task more difficult. We perform extensive experimental evaluation on the SQuAD and MS MARCO datasets using standard metrics. The output of the higher-level encoder is contextual representation for each set of sentences s=\textscSentEnc (~s), where si is the paragraph-dependent representation for the i-th sentence. Question Answering systems have many use cases like automatically responding to a customer’s query by reading through the company’s documents and finding a perfect answer.. Research paper, code implementation and pre-trained model are available to download on the Paperwithcode website link. Automatic Factual Question Generation from Text Michael Heilman CMU-LTI-11-004 Language Technologies Institute School of Computer Science Carnegie Mellon University 5000 Forbes Ave., Pittsburgh, PA 15213 www.lti.cs.cmu.edu Thesis Committee: Vincent Aleven, Carnegie Mellon University William W. Cohen, Carnegie Mellon University By signing up you accept our content policy. However, these texts don't come with the review questions which are crucial in reinforcing one's concept and crafting them themselves can be extremely time-consuming for both teachers as well student. MS MARCO contains passages that are retrieved from web documents and the questions are anonimized versions of BING queries. We first explain the sentence and paragragh encoders (Section 3.3.1) before moving on to explanation of the decoder (Section 3.3.2) and the hierarchical attention modules (HATT and MHATT in Section 3.3.3). The decoder is further conditioned on the provided (candidate) answer to generate relevant questions. gated self-attention networks. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation. The final representation r from last layer of decoder is fed to the linear followed by softmax layer for calculating output probabilities. We postulate that attention to the paragraph benefits from our hierarchical representation, described in Section 3.1. QG at the paragraph level is much less explored and it has remained a challenging problem. We employ the attention mechanism proposed in (Luong et al., 2015) at both the word and sentence levels. In Section C of the appendix, we present some failure cases of our model, along with plausible explanations. This representation is the output of the last encoder block in the case of Transformer, and the last hidden state in the case of BiLSTM. Specifically, the Transformer is based on the (multi-head) attention mechanism, completely discarding recurrence in RNNs. Automatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs. Moreover, the Transformer architecture shows great potential over the more traditional RNN models such as BiLSTM as shown in human evaluation. comprehension. The selective sentence level attention (ast) is computed as: ast=Sparsemax([uwti]Ki=1), where, K is the number of sentences, usti=vTstanh(Ws[gi,dt]). Use Git or checkout with SVN using the web URL. Random questions can be a wonderful way to begin a writing session each day. Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements. As of 2019, Question generation from text has become possible. Similar to the word-level attention, we again the compute attention weight over every sentence in the input passage, using (i) the previous decoder hidden state and (ii) the sentence encoder’s hidden state. (2017) were the first to propose a sequence-to-sequence (Seq2Seq) architecture for QG. On the MS MARCO dataset, the two LSTM-based models outperform the two Transformer-based models. Here, Kiw is the key matrix for the words in the i-th sentences; the dimension of the resulting attention vector bi is the number of tokens in the i-th sentence. Tran et al. A dictionary is created called bucket and the part-of-speech tags are added to it. This paper describes the question generation system devel-oped at UPenn for QGSTEC, 2010. You signed in with another tab or window. Long text has posed challenges for sequence to sequence neural models in question generation – worse performances were reported if using the whole paragraph (with multiple sentences) as the input. Qualitatively, our hierarchical models are able to generate fluent and relevant questions. This program takes a text file as an input and generates questions by analyzing each sentence. In this paper, we present and contrast novel approachs to QG at the level of paragraphs. We take a subset of MS MARCO v1.1 dataset containing questions that are answerable from atleast one paragraph. Interestingly, human evaluation results, as tabulated in Table 3 and Table 4, demonstrate that the hierarchical Transformer model TransSeq2Seq + AE outperforms all the other models on both datasets in both syntactic and semantic correctness. Image from Pixabay and Stylized by AiArtist Chrome Plugin (Built by me). If nothing happens, download GitHub Desktop and try again. The system generates automatic questions given a paragraph and an answer - gsasikiran/automatic-question-generation Existing text-based QG methods can be broadly classified into three categories: (a) rule-based methods, (b) template-base methods, and (c) neural network-based methods. In our case, a paragraph is a sequence of sentences and a sentence is a sequence of words. python -m textblob.download_corpora python3 quest.py file.txt. At the lower level, the encoder first encodes words and produces a sentence-level representation. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. The final context (ct) based on hierarchical selective attention is computed as: ct=∑iasti∑j¯¯¯awti,jri,j, where ¯¯¯awti,j is the word attention score obtained from awt corresponding to jth word of the ith sentence. However, Transformer, as a non-recurrent model, can be more effective than the recurrent model because it has full access to the sequence history. Specifically, we propose a novel hierarchical Transformer architecture. … If you’re in a big hurry and are running out of options due to a deadline. It is already answered, but I want to give you some more opinion. And each sentence is passed as string to function genQuestion(line): These are the part-of-speech tags which is used in this demo. Matthew Lynch Editor of The Edvocate and The Tech Edvocate. Run it on Google Chrome for better performance. This module attempts to automati-cally generate the most relevant as well as syntac-tically and semantically correct questions around To attend to the hierarchical paragraph representation, we replace the multi-head attention mechanism (to the source) in Transformer by introducing a new multi-head hierarchical attention module MHATT(qs,Ks,qw,Kw,Vw) where qs is the sentence-level query vector, qw is the word level query vector, Ks is the key matrix for the sentences of the paragraph, Kw is the key matrix for the words of the paragraph, and Vw is the value matrix fr the words of the paragraph. Ming Zhou. It is clearly understood that generating the test question is the toughest part. The hierarchical models for both Transformer and BiLSTM clearly outperforms their flat counterparts on all metrics in almost all cases. We analyzed quality of questions generated on a) syntactic correctness b) semantic correctness and c) relevance to the given paragraph. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. Xinya Du, Junru Shao, and Claire Cardie. On the MS MARCO dataset, we observe the best consistent performance using the hierarchical BiLSTM models on all automatic evaluation metrics. Visual question generation as dual task of visual question answering. Before doing anything else, generate a random question and then choose to incorporate it into a paragraph or write a paragraph answering the question. 2 Bl st ak, M.: Automatic Question Generation Based on Sentence Structure Analysis rst question generation dataset and participants of this event have competed in two tasks: question generation from sentences and question generation from paragraph. Decoder stack will output a float vector, we can feed this float vector to a linear followed softmax layer to get probability for generating target word. To be able to effectively describe these modules, we will benefit first from a description of the decoder (Section 3.3.2). Natural Language Processing (NLP): Automatic generation of questions and answers from Wikipedia ... 27:33. We split the SQuAD train set by the ratio 90%-10% into train and dev set and take SQuAD dev set as our test set for evaluation. We split train set as 90%-10% into train (71k) and dev (8k) and take dev set as test set (9.5k). Out of the search results, 122 papers were considered relevant after looking at their titles and abstracts. We take the input vector to the softmax function ht−1, when the t-th word in the question is being generated. Kumar et al. In Computer-Aided Generation of Multiple-Choice Tests[3], the authors picked the key nouns in the paragraph and and then use a regular expression to generate the question. At the higher level, the encoder aggregates the sentence-level representations and learns a paragraph-level representation. The importance of being recurrent for modeling hierarchical 56 – 64 . Automation of question generation from sentences. We performed human evaluation to further analyze quality of questions generated by all the models. download the GitHub extension for Visual Studio. Also Transformer is relatively much faster to train and test than RNNs. Let us assume that the question decoder needs to attend to the source paragraph during the generation process. Vishwajeet Kumar, Ganesh Ramakrishnan, and Yuan-Fang Li. We can use pre-tagged bag of words to improve part-of-speech tags. Agarwal, M., Mannem, P.: Automatic Gap-fill Question Generation from Text Books. We the concatenate forward and backward hidden states of the BiLSTM encoder to obtain the final hidden state representation (ht) at time step t. Representation (ht) is calculated as: Thus, for paragraph-level question generation, the hierarchical representation of paragraphs is a worthy pursuit. Recently, Zhao et al. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates … Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. A text file passed as argument to the program. A number of interesting observations can be made from automatic evaluation results in Table 1 and Table 2: Overall, the hierarchical BiLSTM model HierSeq2Seq + AE shows the best performance, achieving best result on BLEU2–BLEU4 metrics on both SQuAD dataset, whereas the hierarchical Transformer model TransSeq2Seq + AE performs best on BLEU1 and ROUGE-L on the SQuAD dataset. At the higher level, our HPE consists of another encoder to produce paragraph-dependent representation for the sentences. For more information, see our Privacy Statement. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. The generated question list is printed as output. Harvesting paragraph-level question-answer pairs from Wikipedia. The current state-of-the-art question generation model uses language modeling with different pretraining objectives. Given an input (e.g., a passage of text in NLP or an image in CV), optionally also an answer, the task of QG is to generate a natural-language question that is answerable from the input. Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made. Zhihao Fan, Zhongyu Wei, Piji Li, Yanyan Lan, and Xuanjing Huang. You can always update your selection by clicking Cookie Preferences at the bottom of the page. One straightforward extension to such a model would be to reflect the structure of a paragraph in the design of the encoder. However, LSTM is based on the recurrent architecture of RNNs, making the model somewhat rigid and less dynamically sensitive to different parts of the given sequence.

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