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. 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. If you’re in a big hurry and are running out of options due to a deadline. 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. 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. There-fore, recognizing, understanding the content of discussion topic clearly, and taking all types of text that are listed in Table 1 as input is the first step of the QGS system. Figure 2), we make use of a Transformer decoder to generate the target question, one token at a time, from left to right. Taking this inspiration, we give the same power to our model by incorporating word-level and sentence-level selective attention to generate high-quality questions from paragraphs. Automatic. Existing question generation methods are typically based on recurrent neural networks (RNN), such as bi-directional LSTM. Neural network based methods represent the state-of-the-art for automatic question generation. By signing up you accept our content policy. Our hierarchical paragraph encoder (HPE ) consists of two encoders, viz., a sentence-level and a word-level encoder; (c.f. We employ the BiLSTM (Bidirectional LSTM) as both, the word as well as the sentence level encoders. 2018. At the lower level, the encoder first encodes words and produces a sentence-level representation. Harvesting paragraph-level question-answer pairs from Wikipedia. We can use pre-tagged bag of words to improve part-of-speech tags. We perform extensive experimental evaluation on the SQuAD and MS MARCO datasets using standard metrics. Similarly, Song et al. In this paper, we describe an end-to-end question generation process that takes as input “context” paragraphs from the Stanford Question Answering Dataset (SQuAD) [14], initially phrase extraction is a vital step to allow automatic question generation to scale beyond datasets with predefined answers to real-world education applications. Zhihao Fan, Zhongyu Wei, Piji Li, Yanyan Lan, and Xuanjing Huang. We describe our models below: Seq2Seq + att + AE is the attention-based sequence-to-sequence model with a BiLSTM encoder, answer encoding and an LSTM decoder. comprehension. The results demonstrate the overall effectiveness of the hierarchical models over their flat counterparts. There are several research papers for this task. (2018) recently proposed a Seq2Seq model for paragraph-level question generation, where they employ a maxout pointer mechanism with a gated self-attention encoder. 2017. We then present two decoders (LSTM and Transformer) with hierarchical attention over the paragraph representation, in order to provide the dynamic context needed by the decoder. 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. questiongeneration.org > Question Generation is the task of automatically generating questions from various inputs such as raw text, database, or semantic representation. 2017. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. Compared to the flat LSTM and Transformer models, their respective hierarchical counterparts always perform better on both the SQuAD and MS MARCO datasets. You can always update your selection by clicking Cookie Preferences at the bottom of the page. The sentence which gets parsed successfully generates a question sentence. See my Quora answers on question generation: * Can computers make questions? We analyzed quality of questions generated on a) syntactic correctness b) semantic correctness and c) relevance to the given paragraph. Lastly, the context vector is computed using the word values of their attention weights based on their sentence-level and word-level attentions: Attention in MHATT module is calculated as: Where Attention(Qw,Vw,Kw) is reformulation of scaled dot product attention of (Vaswani et al., 2017). Kumar et al. That is, our model identifies firstly the relevance of the sentences, and then the relevance of the words within the sentences. Effective approaches to attention-based neural machine translation. This set was further reduced to 36 papers after reading the full text of the papers. The decoder is further conditioned on the provided (candidate) answer to generate relevant questions. Automatic question generation by using NLP. In: Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications, pp. These models do not require templates or rules, and are able to generate fluent, high-quality questions. Keep your question short and to the point. The main challenges in paragraph-level QG stem from the larger context that the model needs to assimilate in order to generate relevant questions of high quality. Textbooks, factoid archives, news articles, reports, lecture notes, legal proceedings -- the minimum viable input is a small to moderate sized … A dictionary is created called bucket and the part-of-speech tags are added to it. The matrices for the sentence-level key Ks and word-level key Kw are created using the output. It is already answered, but I want to give you some more opinion. We also propose a novel hierarchical BiLSTM model with selective attention, which learns to attend to important sentences and words from the paragraph that are relevant to generate meaningful and fluent questions about the encoded answer. The system generates automatic questions given a paragraph and an answer - gsasikiran/automatic-question-generation HierTransSeq2Seq + AE is the hierarchical Transformer model with a Transformer sentence encoder, a Transformer paragraph encoder followed by a Transformer decoder conditioned on answer encoded. Thus the continued investigation of hierarchical Transformer is a promising research avenue. Xinya Du, Junru Shao, and Claire Cardie. SQuAD: 100,000+ questions for machine comprehension of text. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates … Recently, Zhao et al. Visual question generation as dual task of visual question answering. Further, we can add complex semantic rules for creating long and complex questions. We assume that the first and last words of the sentence are special beginning-of-the-sentence <\textscBOS> and end-of-the-sentence <\textscEOS> tokens, respectively. 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. Question generation is the most important part of the teaching-learning process. In contrast, Goldberg (2019) report settings in which attention-based models, such as BERT are better capable of learning hierarchical structure than LSTM-based models. This program uses a small list of combinations. HierSeq2Seq + AE is the hierarchical BiLSTM model with a BiLSTM sentence encoder, a BiLSTM paragraph encoder and an LSTM decoder conditioned on encoded answer. Let us assume that the question decoder needs to attend to the source paragraph during the generation process. MS MARCO contains passages that are retrieved from web documents and the questions are anonimized versions of BING queries. statistical ranking for question generation. The text file is read using a Python package called textblob. Our findings also suggest that LSTM outperforms the Transformer in capturing the hierarchical structure. Automatic question generation (QG) is the task of generating meaningful questions from text. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. The output of the paragraph encoder is transformed into a set of attention vectors Kencdec and Vencdec. However, the hierarchical BiLSTM model HierSeq2Seq + AE  achieves best, and significantly better, relevance scores on both datasets. We use essential cookies to perform essential website functions, e.g. This demo only uses the grammar to generate questions starting with 'what'. Research paper, code implementation and pre-trained model are available to download on the Paperwithcode website link. TransSeq2Seq + AE is a Transformer-based sequence-to-sequence model with a Transformer encoder followed by a Transformer decoder conditioned on encoded answer. We split train set as 90%-10% into train (71k) and dev (8k) and take dev set as test set (9.5k). Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. At the higher level, the encoder aggregates the sentence-level representations and learns a paragraph-level representation. a=softmax(qsKs/d). (2018) encode ground-truth answers (given in the training data), use the copy mechanism and additionally employ context matching to capture interactions between the answer and its context within the passage. SQuAD contains 536 Wikipedia articles and more than 100K questions posed about We denote the resulting sentence representation by ~si for a sentence xi. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. r=FFNN(x)=(max(0,xW1+b1))W2+b2, where 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. This paper describes the question generation system devel-oped at UPenn for QGSTEC, 2010. Question Generation (QG) and Question Answering (QA) are key challenges facing systems that interact with natural languages. In the present study, we propose an automatic question generation for sentences from text passages in reading comprehension. 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). Automatic gap-fill question generation from text books. We use the LSTM decoder’s previous hidden state and the word encoder’s hidden state to compute attention over words (Figure 1). Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 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]). Results demonstrate the hierarchical representations to be overall much more effective than their flat counterparts. 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. Matthew Lynch Editor of The Edvocate and The Tech Edvocate. python3 quest.py file.txt. However, due to their ineffectiveness in dealing with long sequences, paragraph-level question generation remains a challenging problem for these models. In Arikiturri [4], they use a corpus of words and then choose the most relevant words in a given passage to ask questions from. Natural Language Processing (NLP): Automatic generation of questions and answers from Wikipedia ... 27:33. Leveraging context information for natural question generation. Thus, for paragraph-level question generation, the hierarchical representation of paragraphs is a worthy pursuit. It is clearly understood that generating the test question is the toughest part. 2018. We postulate that attention to the paragraph benefits from our hierarchical representation, described in Section 3.1. Zhao et al. We feed the sentence representations ~s to our sentence-level BiLSTM encoder (c.f. 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. Many researchers have worked in the area of automatic question generation through NLP, and numerous techniques and models have been developed to generate the different types of question automatically. As of 2019, Question generation from text has become possible. Question generation (QG) has recently attracted significant interests in the natural language processing (NLP) (Du et al., 2017; Kumar et al., 2018; Song et al., 2018; Kumar et al., 2019) and computer vision (CV) (Li et al., 2018; Fan et al., 2018) communities. We performed all our experiments on the publicly available SQuAD (Rajpurkar et al., 2016) and MS MARCO (Nguyen et al., 2016) datasets. Whoever came up with the idea of marrying AI and question generation is a genius. Your comment should inspire ideas to flow and help the author improves the paper. In this second option (c.f. The current state-of-the-art question generation model uses language modeling with different pretraining objectives. (C:\xampp\htdocs\ or where you installed Xampp) 2)Copy one Paragraph in Text Box and submit it. This program generates questions starting with 'What'. As of 2019, Question generation from text has become possible. In this paper, we present and contrast novel approachs to QG at the level of paragraphs. Learn more. Virtualenv recommended pip install -r requirements.txt python -m textblob.download_corporapython3 quest.py file.txt Use -voption to activate verbose python3 quest.py file.txt -v You can also try inputing any text file. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Learn more. Learning to ask: Neural question generation for reading Each sentence is parsed using English grammar rules with the use of condition statements. The current state-of-the-art question generation model uses language modeling with different pretraining objectives. About. Du et al. Firstly, this module attends to paragraph sentences using their keys and the sentence query vector: Do try Quillionz for free. Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements. The context vector ct is fed to the decoder at time step t along with embedded representation of the previous output. The importance of being recurrent for modeling hierarchical 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 2018. 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: We also present attention mechanisms for dynamically incorporating contextual information in the hierarchical paragraph encoders and experimentally validate their effectiveness. We present human evaluation results in Table 3 and Table 4 respectively. As before, we concatenate the forward and backward hidden states of the sentence level encoder to obtain the final hidden state representation. Rule based methods (Heilman and Smith, 2010) perform syntactic and semantic analysis of sentences and apply fixed sets of rules to generate questions. Output of the HATT module is passed to a fully connected feed forward neural net (FFNN) for calculating the hierarchical representation of input (r) as: 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. In our case, a paragraph is a sequence of sentences and a sentence is a sequence of words. Quillionz - AI-Powered Automatic Question Generator - Duration: 5:14. Moreover, the Transformer architecture shows great potential over the more traditional RNN models such as BiLSTM as shown in human evaluation. (2018) proposed a paragraph-level QG model with maxout pointers and a gated self-attention encoder. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. BLEU: a method for automatic evaluation of machine translation. We can also activate the verbose mode by -v argument to further understand the question generation process. bi=softmax(qwKiw/d). The automatic question generation is an important research area which is potentially useful in intelligent tutoring systems, dialogue systems, educational technologies, instructionalgames etc. Yes. The potential benefits of using automated systems to generate questions helps reduce the dependency on humans to generate questions and other needs associated with systems interacting with natural languages. Vishwajeet Kumar, Ganesh Ramakrishnan, and Yuan-Fang Li. Specifically, the Transformer is based on the (multi-head) attention mechanism, completely discarding recurrence in RNNs. In Table 1 and Table 2 we present automatic evaluation results of all models on SQuAD and MS MARCO datasets respectively. 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. Also LSTM models are slower to train. I would recommend it to any teacher or school looking to efficiently create assessments, without making a massive dent in their wallets. Let us denote the i-th sentence in the paragraph by xi, where xi,j denotes the j-th word of the sentence. Thang Luong, Hieu Quang Pham, and Christopher D. Manning. Paragraph encoder takes concatenation of word representation of the start word and end word as input and returns paragraph representation. With more question answering (QA) datasets like ... paragraph, end of paragraph and end of question respectively, the task of question generation is to maximize the likelihood of Qgiven Pand s a. The lower-level encoder WordEnc encodes the words of individual sentences. Also Transformer is relatively much faster to train and test than RNNs. gated self-attention networks. Good question! Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. For multiple heads, the multihead attention z=Multihead(Qw,Kw,Vw) is calculated as: where  hi=Attention(QwWQi,KwWKi,VwWVi), WQi∈Rdmodel×dk, WKi∈Rdmodel×dk , WVi∈Rdmodel×dv, WO∈Rhdv×dmodel, dk=dv=dmodel/h=64. The question generation module is a sequence-to-sequence model with dynamic dictio-naries, reusable copy attention and global sparse-max attention. Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Husam Ali, Yllias Chali, and Sadid A Hasan. You signed in with another tab or window. … Automatic Question Generation from paragraph Students are nowadays increasingly turning to online texts to supplement classroom material. z is fed to a position-wise fully connected feed forward neural network to obtain the final input representation. Yikang Li, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang, and They employ an RNN-based encoder-decoder architecture and train in an end-to-end fashion, without the need of manually created rules or templates. * How do I generate questions from corpus or comprehensions using NLP concepts? The feedback must be of minimum 40 characters and the title a minimum of 5 characters, This is a comment super asjknd jkasnjk adsnkj, The feedback must be of minumum 40 characters. The hidden state (gt) of the sentence level encoder is computed as: gt=\textscSentEnc (gt−1,[~st,fst]), where fst is the embedded feature vector denoting whether the sentence contains the encoded answer or not. Question Generation In this module the actual work of generating questions takes place. Research paper, code implementation and pre-trained model are available to download on the Paperwithcode website link. r is fed as input to the next encoder layers. 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. Using AI and NLP it is possible to generate questions from sentenses or paragraph. The Transformer (Vaswani et al., 2017) is a recently proposed neural architecture designed to address some deficiencies of RNNs. EssaySoft Essay Generator takes an essay question and keywords as input, and generates creative high quality essay articles that are free of plagiarism, fully automatic in just a few seconds. This Automatic Gap-Fill Question Generation system creates multiple choice, fill-in-the-blank questions from text corpora. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. The hierarchical models for both Transformer and BiLSTM clearly outperforms their flat counterparts on all metrics in almost all cases. This encoder produces a sentence-dependent word representation ri,j for each word xi,j in a sentence xi, i.e., ri=\textscWordEnc (xi). Better yet, a reword a paragraph generator may also offer their goods in a number of different ways. Automating reading comprehension by generating question and answer If nothing happens, download GitHub Desktop and try again. 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. The sentence encoder transformer maps an input sequence of word representations x=(x0,⋯,xn) to a sequence of continuous sentence representations r=(r0,⋯,rn). 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. Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made. For evaluating our question generation model we report the standard metrics, viz., BLEU (Papineni et al., 2002) and ROUGE-L(Lin, 2004). Existing efforts at AQG have been limited to short answer lengths of up to two or three sentences. 2018. Glove: Global vectors While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model. As humans, when reading a paragraph, we look for important sentences first and then important keywords in those sentences to find a concept around which a question can be generated. This results in a hierarchical attention module (HATT) and its multi-head extension (MHATT), which replace the attention mechanism to the source in the Transformer decoder. The decoder stack is similar to encoder stack except that it has an additional sub layer (encoder-decoder attention layer) which learn multi-head self attention over the output of the paragraph encoder. There are several research papers for this task. We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. Specifically, we propose a novel hierarchical Transformer architecture. It uses complex AI algorithms to generate questions. 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. Qualitatively, our hierarchical models are able to generate fluent and relevant questions. Searching the databases and AIED resulted in 2,012 papers and we checked 974.Footnote 7 The difference is due to ACM which provided 1,265 results and we only checked the first 200 results (sorted by relevance) because we found that subsequent results became irrelevant. 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. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 56 – 64 . Random questions can be a wonderful way to begin a writing session each day. Encoder-decoder attention layer of decoder takes the key Kencdec and value Vencdec . This module attempts to automati-cally generate the most relevant as well as syntac-tically and semantically correct questions around 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. For example: Tom ate an orange at 7 pm To The input to this encoder are the sentence representations produced by the lower level encoder, which are insensitive to the paragraph context. Here, d is the dimension of the query/key vectors; the dimension of the resulting attention vector would be the number of sentences in the paragraph. Before doing anything else, generate a random question and then choose to incorporate it into a paragraph or write a paragraph answering the question. Putting the horse before the cart: A generator-evaluator framework Automation of question generation from sentences. From the evaluation results, we can see that our proposed hierarchical models demonstrate benefits over their respective flat counterparts in a significant way. Paraphrase Online Paraphrasing Tool - The Best Free Article, Sentence and Paragraph Rephrasing Software! No matter what essay topic you have been given, our essay generator will be able to complete your essay without any hassle. Each encoder layer is composed of two sub-layers namely a multi-head self attention layer (Section 3.3.3) and a position wise fully connected feed forward neural network (Section 3.3.4). To the best of our knowledge this is the only model that is designed to support paragraph-level QG and outperforms other models on the SQuAD dataset (Rajpurkar et al., 2016). The word level attention (awt) is computed as: awt=Softmax([uwti]Mi=1), where M is the number of words, and uwti=vTwtanh(Ww[hi,dt]) and dt is the decoder hidden state at time step t. We calculate sentence representation (~si) using word level encoder’s hidden states as: ~si=1|xi|∑jri,j, where ri,j is the word encoder hidden state representation of the jth word of the ith sentence. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words. 2014. Further, our experimental results validate that hierarchical selective attention benefits the hierarchical BiLSTM model. (2018) contrast recurrent and non-recurrent architectures on their effectiveness in capturing the hierarchical structure. Ms marco: A human generated machine reading comprehension dataset. generation. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan 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. We can use a dataset of text and questions along with machine learning to ask better questions. In reality, however, it often requires the whole paragraph as … Out of the search results, 122 papers were considered relevant after looking at their titles and abstracts.