Unsupervised question generation. Inspired by the prior seminal work on this theme [5] .

Unsupervised question generation The model incorporates a cover-age mechanism to alleviate the common issue of answer-related content being left out from the gen-eratedquestions. 2022. Fabbri et al. Request PDF | On Jan 1, 2022, Dinesh Nagumothu and others published PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora | Find, read and cite all the Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering, by Alexander R. Authors: Yingchao Ju, Subramanian, S. MRQA@EMNLP 2019. Semantic Graphs for Generating Deep Questions, by Liangming Pan and Yuxi Xie and Yansong Feng and Tat{-}Seng Chua and Min{-}Yen Kan • Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Unsupervised Question Answering by Cloze Translation Patrick Lewis, Ludovic Denoyer, Sebastian Riedel (ACL 2019) CS 395T: Topics in Natural Language Processing. Question Answering (QA) is in increasing demand as the amount of information available Unsupervised Domain Adaptation for Question Generation with Domain Data Selection and Self-training Peide Zhu and Claudia Hauff Delft University of Technology {p. , unsupervised multi-hop QA. For supervised training, several popular Unsupervised Open-Domain Question Answering Pengfei Zhu Shanghai JiaoTong University zhupf97@sjtu. Question: Kendall frightened the dogs away by yelling and waving his hands. Question Generation (QG) is a challenging Natural Language Processing (NLP) Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. edu Their question-generation framework generates training data from Wikipedia excerpts. We use this model to generate a question q^for an unsupervised passages pu sampled from the target domain distribution PT (p). T1 - Unsupervised Domain Adaptation for Question Generation with Domain Data Selection and Self-training. We find that back-training vastly outperforms self-training by a mean improvement of 7. %0 Conference Proceedings %T Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation %A Xu, Shicheng %A Pang, Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering - unsupervised-qa/README. Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. Annotating long-document question answering (long-document QA) pairs is time-consuming and expensive. In: Proceedings of the Workshop on Machine Reading for Question Answering, pp. Both the question and caption are transformed into constituency parse trees. Bitew et al. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. To address this Binary Classification task, question-answering unsupervised-learning question-generation amazon-ai-labs Updated Jan 22, 2021; Python; p208p2002 / Transformer-QG-on-SQuAD Star 47. Obtaining training data for multi-hop question answering XLM contains the code to train (Unsupervised) Seq2Seq models, based on the code from XLM. Knowledge of Aaron, along with his brother Moses, comes exclusiv ely from religious texts, such as the Bible and Quran. 6\% top-20 retrieval accuracy across both domains. Early KBQG approaches are template-based methods. 24, 2021) Introduction Data Tagging Pipeline Unsupervised Data Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. Question Generation (QG) is the task of generating a plausible question for a given pair. Unsupervised Question Answering via Answer Diversifying. A Study on Unsupervised Question and Answer Generation for Legal Information Retrieval and Precedents Understanding. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. However, existing UQA tasks are based on short documents, and Abstract: We focus on the task of Frequently Asked Questions (FAQ) retrieval. To generate such triples, we first sample random context paragraphs from a large corpus of documents and then random noun phrases or named entity mentions from Unsupervised question answering (UQA) We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Multi-hop Question Generation. However, this Question generation systems aim to generate natural language questions that are relevant to a given piece of text, Ramesh Nallapati, and Bing Xiang. Auli. We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically. Inspired by the prior seminal work on this theme [5] Question generation using state-of-the-art Natural Language Processing algorithms. generate_synthetic_qa_data. In recent years, the introduction of neural generation models has resulted in substantial improvements of automatically generated questions in terms of quality, especially compared to traditional approaches that employ Request PDF | On Jan 1, 2020, Junmo Kang and others published Regularization of Distinct Strategies for Unsupervised Question Generation | Find, read and cite all the research you need on ResearchGate Question generation systems aim to generate natural language questions that are relevant to a given piece of text, Ramesh Nallapati, and Bing Xiang. We propose MQA-QG, an unsupervised question answering %0 Conference Proceedings %T Auto-Encoding Questions with Retrieval Augmented Decoding for Unsupervised Passage Retrieval and Zero-Shot Question Generation %A Varanasi, Stalin %A Butt, Muhammad Umer Question Generation (QG) is the task of generating a plausible question for a given &lt;passage, answer&gt; pair. We made the following changes/additions: Unsupervised stopping criterion; Tensorboard logging; Data preprocessing scripts; Minor bug fixes from original XLM code %0 Conference Proceedings %T PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora %A Nagumothu, Dinesh %A Ofoghi, Bahadorreza %A Huang, Guangyan %A Eklund, Peter %Y Fokkens, Antske %Y Srikumar, Vivek %S Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) %D 2022 %8 Language Models, Question and Answer Generation, Precedents Understanding, Unsupervised Learning, Legal Research, Legal Do-main ACM Reference Format: Johny Moreira, Altigran da Silva, Edleno de Moura, and Leandro Marinho. (2020)developedamore on knowledge-based distractor generation in edu-cation eld. use an out-of-domain dataset for question generation, namely, they require additional training sources beyond what can be provided by the target corpus and a pre-trained generic model. The phrase-level constituents in the caption correspond to the different objects in the image, which are shown with different colors. Unsuper-vised question answering (unsupervised QA) be-comes attractive among researchers recently (Lewis et al. Before, Kendall wanted to protect their id text title 1 " Aaron Aaron ( or ; " " Ahärôn " ") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. In some cases, a set of questions may also be provided as part of the dataset; however the true answers to Traditional variational question generation Our vision: a good question generator maximizes mutual information with an expected answer Our final architecture: a variational question generator that maximizes mutual information but doesn’t need to know the expected answer Figure 2. edu Our re-ranking approach is unsupervised, i. Paper BibTeX. UPR uses a pre-trained language model to score the probability of generating the question q given the passage text z , as described below. edu, fpatricng,zhiguow, rnallapa, bxiangg@amazon. paper Devang Kulshreshtha, Robert Belfer, Iulian Vlad Request PDF | On Jan 1, 2022, Dinesh Nagumothu and others published PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora | Find, read We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data UQG: excessive copying of the context in question generation and unanswerable questions, 2) methods to mitigate the problems, which together generate higher quality questions, and 3) Our experimental results on unsupervised domain adaptation of question generation and passage retrieval models from $\textit{Natural Questions}$ domain to the machine might be possible to generate long-document QA pairs via unsupervised question answer-ing (UQA) methods. This work proposes MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources and shows that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. This paradigm, however, relies on scarce, and costly to obtain, We propose a pretrained language model based evaluation metric called QAScore, which is unsupervised and reference-free. 3. AnswerMulti-hop Questions 6 When did the rock band that Request PDF | On Jan 1, 2021, Liangming Pan and others published Unsupervised Multi-hop Question Answering by Question Generation | Find, read and cite all the research you need on ResearchGate We propose an unsupervised question generation method which uses summarization data 1) To minimize the lexical overlap between passage and question 2) To provide a QA-dataset-independent way of generating questions Our unsupervised QA extrinsic evaluation shows that our method substantially outperforms previous methods Unsupervised Question Answering. fabbri@yale. edu {wenhuchen, xwhan, william}@cs. We focus on the task of Frequently Asked Questions (FAQ) retrieval. nl Abstract Question generation (QG) approaches based on large neural models require (i) large-scale and (ii) high-quality training data. Such a model is useful in generating clarifying options. Evidence retrieval is a crucial step in question answering (QA) tasks, which can filter original context to provide supporting evidence for reading comprehension and reduce the time of answer inference. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. question-answering question-generator question-generation question-gen. Download Citation | On Jan 1, 2022, Peide Zhu and others published Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training | Find, read and cite all the id text title 1 " Aaron Aaron ( or ; " " Ahärôn " ") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. The contributions of this paper are as follows: 1. edu In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Question Generation Question Generation (QG) is a task where models receive context passages c and an-swers a, then generate the corresponding questions q which are expected to be semantically relevant to the context c and answers a [5,31]. The question generation model is zero-shot, Abstract. Lewis et al. We refer to it as UPR , for Unsupervised Pas-sage Re-ranking . proposed EQG-RACE to generate examination-type questions for educational purposes. This script will allow you to generate unsupervised question answering data using the identity, noisy cloze or unsupervised NMT methods explored in the paper, as well as specifying several different configurations (i. Fabbri*y 1,2 Patrick Ng*z1 Zhiguo Wang*z Ramesh Our experimental results on unsupervised domain adaptation of question generation and passage retrieval models from $\textit{Natural Questions}$ domain to the Traditional variational question generation Our vision: a good question generator maximizes mutual information with an expected answer Our final architecture: a variational question additional, synthetic questions, for each ground-truth answer. The two models match user queries to FAQ answers and questions, respectively. : Neural models for key phrase extraction and question generation. Most research interests in evidence retrieval have been paid to supervised methods, while unsupervised evidence retrieval received limited attention. Unsupervised Natural Language Inference Using PHL Triplet Generation Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale, Chitta Baral Arizona State University {nvarshn2, pbanerj6, tgokhale Unsupervised Question-Answering: The un-supervised paradigm where no human-annotated training data is provided for learning has mostly been explored for AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking . 2020. MQA-QG This work introduces two approaches to improve unsupervised QA, which harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to This work introduces two approaches to improve unsupervised QA, which harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of Pan et al. but USE is not a purely unsupervised model : “We augment unsupervised learning with training on supervised data from the Stanford Natural Lan-guage Inference (SNLI) corpus” (Bowman et al. Maetal. We propose to tackle this by re-duction to unsupervised question generation: If we had a method, without using QA supervision, to generate accurate questions given a context docu- Unsupervised Multi-hop Question Answering by Question Generation Liangming Pan, WenhuChen, WenhanXiong, Min-Yen But how can we generate multi-hop questions without seeing any human-written examples? Zero-shotMulti-hop QA. A novel regularization method based on teacher-student architecture is proposed to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated to generate higher-quality questions for UQA. The difference is that in the unsupervised setting, Unsupervised Multi-hop Question Answering by Question Generation Liangming Pan1 Wenhu Chen 2Wenhan Xiong Min-Yen Kan1 William Yang Wang2 1School of Computing, National University of Singapore, Singapore 2University of California, Santa Barbara, CA, USA e0272310@u. Xiang. However, existing UQA tasks are based on short documents, and can hardly This work introduces two approaches to improve unsupervised QA, which harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a This work proposes AttenWalker, a novel unsupervised method to aggregate and generate answers with long-range dependency so as to construct long-document QA pairs, and Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering Alexander R. Google Scholar [64] Angela Fan, David Grangier, and M. edu. Fabbri and Patrick Ng and Zhiguo Wang and Ramesh Nallapati and Bing Xiang. COLING 2020. com Abstract We focus on the task of Frequently Asked Questions (FAQ) retrieval. nus. , Lewis et al [] explored several unsupervised methods Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. Google Scholar Existing unsupervised QA system techniques such as Lewis et al. Relevance of unsupervised Abstract. In recent years, the introduction of neural generation models has resulted in substantial improvements of automatically generated questions in terms of quality, especially compared to traditional approaches that employ In this work we address the above question by exploring the idea of unsupervised EQA, a setting in which no aligned question, context and answer data is available. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a DOI: 10. use an out-of-domain dataset for question generation, namely, they require additional training Yosi Mass, Boaz Carmeli, Haggai Roitman, David Konopnicki, 2020, Unsupervised FAQ Retrieval with Question Generation and BERT, in proceedings of the 58th Annual Meeting of the Unsupervised Question Answering. Thus QG is a reverse/dual task of QA as QA aims to provide answers a to questions q whereas QG Unsupervised Multi-hop Question Answering by Question Generation Liangming Pan1 Wenhu Chen 2Wenhan Xiong Min-Yen Kan1 William Yang Wang2 1School of Computing, National University of Singapore, Singapore 2University of California, Santa Barbara, CA, USA e0272310@u. 2017. Existing liter-ature for long-form question generation focuses mostly on reading comprehension assessment, and does not use semi-structured metadata Existing unsupervised QA system techniques such as Lewis et al. QG can To address this issue, this paper proposes an unsupervised question retrieval method called QRTM (Question Retrieval based on Topic Filtering and Multi-Task Learning). One approach to UQA is to train a QA model with questions A summary of must-read papers for Neural Question Generation (NQG) Unsupervised Question Answering by Cloze Translation ACL, 2019. com Question Generation (QG) aims to automate the task of composing questions for a passage with a set of chosen answers found within the passage. These two ple of the question generation process we propose (called PIE-QG for Paraphrasing, Information Ex-traction Question Generation) is shown in Figure1. , it does not use any task-specic training exam-ples. 4 F1 on SQuad v1. A given user query can be matched against the questions and/or the answers in the FAQ. We use an unsupervised generator-discriminator model based on cloze translation to generate answerable questions, following the work by Lewis et al. QA Experiments: Improved Unsupervised QA via improved Question Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation. Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results. Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering Alexander R. g. Besides, we propose AttenWalker, a This work proposes AttenWalker, a novel unsupervised method to aggregate and generate answers with long-range dependency so as to construct long-document QA pairs, and outperforms previous methods on Qasper and NarrativeQA. edu We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. 1. 2 Methodology 2. Code Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. kr Abstract Unsupervised question answering (UQA) has been proposed to avoid the high cost of cre- Abstract. In this work, we propose an approach for generating a fine-tuning dataset thanks to a rule-based algorithm that generates questions and answers from unannotated sentences. AU - Zhu, Peide. Published in ACL 2023 Findings, 2023. Controllable abstractive summarization. pletely unsupervised models for question answer-047 ing. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval. We describe the PIE-QG method in which paraphrased passages from the original cor-pus are used to generate question-answer Unsupervised Multi-hop Question Answering by Question Generation Liangming Pan1 Wenhu Chen 2Wenhan Xiong Min-Yen Kan1 William Yang Wang2 1School of Computing, National University of Singapore, Singapore 2University of California, Santa Barbara, CA, USA e0272310@u. One commonly used dataset for unsuper-048 vised question answering is the extractive dataset 049 SQUAD (Rajpurkar et al. Note that q^ is gen-erated conditioned on the target domain passage using PS(qjpu). Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA Unsupervised Question Edition Figure 3. 3390/e24111514 Corpus ID: 252781032; QAScore—An Unsupervised Unreferenced Metric for the Question Generation Evaluation @article{Ji2022QAScoreAnUU, title={QAScore—An Unsupervised Unreferenced Metric for the Question Generation Evaluation}, author={Tianbo Ji and Chenyang Lyu and Gareth J. 100 outputs are sampled and three expert raters are required to score these outputs in three dimensions: fluency—whether a question is grammatical and fluent; relevancy—whether the question is semantically relevant to the passage; and answerability—whether the question can 2. Since multiple questions are possible for any image, previous We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. ibm. 2018. Patrick Lewis, Cesare Campagnano - Unsupervised Approaches for Question Answering - Sapienza NLP reading group (Mar. ac. To alleviate the Question generation (QG) is a natural language processing (NLP) problem that aims to generate natural questions from a given sentence or paragraph. 050 (2019) proposed a question generation method for 051 SQUAD using an unsupervised neural based trans-052 lation method. ,2019): l a= X t logp(A tjC t) (3) 4 Iterative Pre-train We focus on the task of Frequently Asked Questions (FAQ) retrieval. To provide labels for a new domain dataset, question generation is commonly used to create synthetic data consisting of question-answer pairs from documents of the target domain Request PDF | On Jan 1, 2022, Guillaume Le Berre and others published Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning | Find, read and cite all the research you Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. Specifically, we first select present candidate noun phrases from a given text document based on POS tags. ) flask text-generation api-server Unsupervised FAQ Retrieval with Question Generation and BERT Yosi Mass, Boaz Carmeli, Haggai Roitman and David Konopnicki IBM Research AI Haifa University, Mount Carmel, Haifa, HA 31905, Israel fyosimass,boazc,haggai,davidkog@il. (2020) andLi Regularization of Distinct Strategies for Unsupervised Question Generation Junmo Kang Giwon Hong Haritz Puerto San Roman Sung-Hyon Myaeng School of Computing, KAIST Daejeon, Republic of Korea fjunmo. We consider four NLG tasks (text summarization, question gen-eration, news headline generation, and dis-tractor generation) and three syntactically di-verse languages, i. 1 Generator In this paper, we propose an unsupervised question re-trieval approach TFCSG (An Unsupervised Question Re-trieval Based on Latent T opic F iltering and Multi-task Learning). Updated Dec 8, 2023; teacherpeterpan / Unsupervised-Multi-hop-QA. In this study, we propose a new task, named unsupervised long-document question answering (ULQA), aiming to generate high-quality long-document QA instances in an unsupervised manner. However, existing UQA tasks are based on short documents, and can hardly incorporate long-range information. . In this paper, we present an unsupervised question-retrieval approach TFCSG (unsupervised question-retrieval based on Topic keywords Filtering and multi-task learning With Contrastive learning, Similar distribution of question and sequential topic keywords, and Generation of sequential topic keywords from questions) based on keyword filter and multi Jia et al. E. arXiv preprint arXiv:2004. QG has many applications, especially in education. PDF | Neural question generation The unsupervised pre-training models, such as BERT [33], GPT [34], BART [35] and T5 [36], have accomplished great success in NLP because unlabeled text. Instead, Bibliographic details on Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning. 78–88 (2018) The re-ranker re-scores retrieved passages with a zero-shot question generation model, When evaluated on a number of open-domain retrieval datasets, our re-ranker improves strong unsupervised retrieval models by 6%-18% absolute and strong supervised models by up to 12% in terms of top-20 passage retrieval accuracy. In contrast, we propose the first framework for unsupervised multihop QA, which can leverage disjoint structured or unstructured data sources to answer complex questions requiring reasoning. This paradigm, however, relies on scarce, and costly to obtain, large-scale human Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning === 先由人工給定一個主題，程 NCHU-NLPlab Linked with GitHub We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically. Nasution(2023) %0 Conference Proceedings %T TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering %A Sun, Yueqing %A Zhang, Yu %A Qi, Le %A Shi, Qi %Y Goldberg, Yoav %Y Kozareva, Zornitsa %Y Zhang, Yue %S Findings of the Association for Computational Linguistics: EMNLP 2022 %D 2022 %8 December %I as Multiple-Choice Question generation [12] or a pre-emptive approach in which viable IE patterns are created in advance without human intervention [20,15]. On the other hand, rule-based QA system methods, those constrained to generate question-answer pairs %0 Conference Proceedings %T Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval %A Kulshreshtha, Devang %A Belfer, Robert %A Serban, Iulian Vlad %A Reddy, Siva %Y Moens, Marie-Francine %Y Huang, Xuanjing %Y Specia, Lucia %Y Yih, Scott Wen-tau %S Proceedings of the 2021 Conference on Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. In problem, it might be possible to generate long-document QA pairs via unsupervised question answering (UQA) methods. Unsupervised QA systems generate their own question-answer training pairs, typically A fully unsupervised method is presented that exploits the FAQ pairs to train two BERT models that match user queries to FAQ answers and questions, respectively and shows that the model is on par and even outperforms supervised models on existing datasets. Doughty et al. Junmo Kang*, Giwon Hong*, Haritz Puerto San Roman*, Sung-Hyon Myaeng. kang, gch02518, haritzpuerto, myaengg@kaist. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an question-answering unsupervised-learning question-generation amazon-ai-labs Updated Jan 22, 2021; Python; p208p2002 / Transformer-QG-on-SQuAD Star 47. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i. hauff}@tudelft. This paradigm, however, relies on scarce, and costly to obtain, large-scale human-labeled data. We use the pairs (pu, q^) as the synthetic training data to adapt PS(qjp) to the tar For unsupervised question generation as f prompt (x), a static template pattern is defined. Previous works usually make use of heuristic rules as well as pre-trained models Specifically, for each question, we first generate self-contained questions as reasoning question prompts via an unsupervised question edition module considering Summary generation is an important research direction in natural language processing. Instructions: For the following knowledge and question, generate the answer to the question. Recent studies on open-book QG, where supportive question-context pairs are Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents. arXiv, 2021. 2. We We explore the suitability of unsupervised representation learning methods on biomedical text-BioBERT, SciBERT, and Bio-SentVec-for biomedical question answering. (2021) raise the unsupervised shortdocument multi-hop question answering (UMQA) task and design a question generation method to build multi-hop questions We propose Multi-Hop Question Generator (MQA-QG), a simple yet general framework that decomposes the generation of a multi-hop question into two steps: 1) selecting relevant We propose an unsupervised question generation method which uses summarization data 1) To minimize the lexical overlap between passage and question 2) To provide a QA-dataset We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. Download Citation | On Jan 1, 2020, Ethan Perez and others published Unsupervised Question Decomposition for Question Answering | Find, read and cite all the research you need on ResearchGate %0 Conference Proceedings %T Auto-Encoding Questions with Retrieval Augmented Decoding for Unsupervised Passage Retrieval and Zero-Shot Question Generation %A Varanasi, Stalin %A Butt, Muhammad Umer Question Generation (QG) is the task of generating a plausible question for a given \textlesspassage, answer\textgreater pair. whether to use subclause additional, synthetic questions, for each ground-truth answer. It can be used to generate tags for any questions for which student explanations are available. Y1 - 2022. While these methods may reduce the reliance on manual effort to a certain extent, their scalability Unsupervised Multi-hop Question Answering by Question Generation Liangming Pan1 Wenhu Chen 2Wenhan Xiong Min-Yen Kan1 William Yang Wang2 1School of Computing, National University of Singapore, Singapore 2University of California, Santa Barbara, CA, USA e0272310@u. Authors: Johny Moreira, Altigran da Silva, Edleno de Moura, Leandro Marinho Authors Info & Claims. Code Issues Pull requests Implement Question Generator with SOTA pre-trained Language Models (RoBERTa, BERT, GPT, BART, T5, etc. Like supervised extractive Recently, some unsupervised methods have been proposed for Extractive Question Answering (EQA) tasks. (2024) designs complex prompt to generate multi-choice question answering data for Python programming learning. 11892 (2020). We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop You can generate reading comprehension training data using unsupervisedqa. Unsupervised question answering (UQA) has been proposed to avoid the high cost of You can generate reading comprehension training data using unsupervisedqa. ucsb. , English, Hindi, and Japanese. 1 Question Generation In order to avoid generating trivial questions that are highly similar to PDF | On Jan 1, 2020, Steven Rennie and others published Unsupervised Adaptation of Question Answering Systems via Generative Self-training | Find, read and cite all the research you need Unsupervised Multi-hop Question Answering by Question Generation Liangming Pan1 Wenhu Chen 2Wenhan Xiong Min-Yen Kan1 William Yang Wang2 1School of Computing, National We present MinPrompt, a minimal data augmentation framework for open-domain QA based on an approximate graph algorithm and unsupervised question generation. , 2015). Google Scholar might be possible to generate long-document QA pairs via unsupervised question answer-ing (UQA) methods. Request PDF | On Jul 11, 2024, Johny Moreira and others published A Study on Unsupervised Question and Answer Generation for Legal Information Retrieval and Precedents Understanding | Find, read Automatic Question Generation For unsupervised training, it is trained on a novel 750 Gigabyte-huge dataset, the Colossal Clean Crawled Corpus(C4) , which was created especially for T5. cn Xiaoguang Li Huawei Noah’s Ark Lab lixiaoguang11@ huawei. , and then alter the model to enable it to generate unanswerable questions. QAScore utilizes the RoBERTa model [20], and evaluates a system-generated question using the We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. A given user query can be matched against the Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering Alexander R. We generate synthetic QA data that is further employed to train an unsupervised QA model. Our method UST is an unsupervised technique for automatic tag generation and does not make use of any additional input other than the student explanation text and the master tag list from which concepts are matched. In this work, we explore to what extent high quality training data is actually required for Extractive QA, and investigate the possibility of unsupervised Extractive QA. 6% top-20 retrieval accuracy across both domains. (2023) explores question-similarity based example selection method to en-hance LLMs' DG performance in in-context learn-ing. You switched accounts on another tab or window. To tackle the problem, we propose a new task, named unsupervised long-document question answering (ULQA), aiming to generate high- Question generation. Cloze Generation. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their Regularization of Distinct Strategies for Unsupervised Question Generation. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. Question Generation (QG) aims to automatically generate questions from textual inputs (Pan et al. N2 - Question generation (QG) approaches based on large neural models require (i) large-scale and (ii) high-quality training data. and Lyu et al. [] proposed a straightforward method, which utilize heuristic Unsupervised Question Decomposition for Question Answering Ethan Perez1 2 Patrick Lewis1 3 Wen-tau Yih1 Kyunghyun Cho1 2 4 Douwe Kiela1 Abstract We aim to improve question • Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Unsupervised Question Answering by Cloze Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering. Unsupervised Question Decomposition for Question Answering Ethan Perez1 2 Patrick Lewis1 3 Wen-tau Yih1 Kyunghyun Cho1 2 4 Douwe Kiela1 Abstract We aim to improve question answering (QA) we would require sophisticated natural language generation capabilities, which often relies on large quantities of high-quality supervised data. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) An overview of our approach where Answer and Question are generated based on Summary by the Question Generation Heuristics, the Answer is combined with the Article to form the input to the A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. [] proposed a straightforward method, which utilize heuristic templates. The You signed in with another tab or window. Junmo Kang*, Haritz Puerto San Roman*, Sung-Hyon Myaeng. , et al. com Unsupervised question answering (UQA) We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Examples: Knowledge: Parents will protect their children from dog attacks. Unsupervised domain adaptation is an approach to cope with transferring knowledge from a source domain to a different unlabeled target domain (Pan and Yang,2010). paper. A given user query that can generate a question qgiven a passage p. A common approach to QA has been to Unsupervised commonsense question answering requires mining effective commonsense knowledge without the rely on the labeled task data. It is found that modern QA models can learn to answer human questions surprisingly well using only synthetic training data, and is demonstrated that, without using the SQuAD training data at all, this approach achieves 56. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer. PY - 2022. Song et al. TFCSG uses Unsupervised Question Answering via Answer Diversifying . Junmo Kang, Giwon Hong, Haritz Puerto, Sung-Hyon Myaeng. In order to overcome these shortcomings, we propose a distantly-supervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. However, here we expand upon the recently introduced task of Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval. Chen et al. md at master · awslabs/unsupervised-qa %0 Conference Proceedings %T Learning to Ask: Neural Question Generation for Reading Comprehension %A Du, Xinya %A Shao, Junru %A Cardie, Claire %Y Barzilay, for technical question generation. Question Generation (QG) aims to automate the task of composing questions for a passage with a set of chosen answers found within the passage. In NMT@ACL Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. Weiping Fu 1,4, Bifan Wei 2,4 † † thanks: Corresponding author, Jianxiang Hu 1,4, Zhongmin Cai 3,4, Jun Liu 1,4 1 School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China 2 School of Continuing Education, Xi’an Jiaotong University, Xi’an, China 3 MOE KLINNS Lab & School Knowledge Base Question Generation. Template-based QG uses linguistically-informed heuristics to Jia et al. Obtaining the context; Advancements in unsupervised learning for question answering will provide various useful applications in different domains. Template-based QG uses linguistically-informed heuristics to transform Unsupervised question answering is an attractive task due to its independence on labeled data. Methods (i) and (ii) were not fine-tuned on the question answering task (since this would vio-late the spirit of this unsupervised-only system), Unsupervised Keyphrase Generation by Utilizing Masked Words Prediction and Pseudo-label BART Finetuning. AU - Hauff, Claudia. Most techniques that operate fully unsupervised also train an answer prior in the same manner, which generates answers for a %0 Conference Proceedings %T DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering %A Ke, Pei %A Huang, Fei %A Mi, Fei %A Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training @inproceedings{Zhu2022UnsupervisedDA, title={Unsupervised Domain Adaptation Regularization of Distinct Strategies for Unsupervised Question Generation. (2019) presented an answer-aware question generation (QG) model that employs reinforcement learning for improved question quality. ,2016). Towards Zero-Shot Multilingual Synthetic %0 Conference Proceedings %T Unsupervised Question Answering by Cloze Translation %A Lewis, Patrick %A Denoyer, Ludovic %A Riedel, Sebastian %Y Korhonen, 2 Unsupervised Question Answering Problem Setup: In the unsupervised question an-swering setup, typically, a dataset of context para-graphs is available, and the model must learn to For example, the question generated from the sentence Stephen Hawking announced Chapter 4. Fabbriy 1,2 Patrick Ngz1 Zhiguo Wangz Ramesh Nallapatiz Bing Xiangz [y]Yale University [z]AWS AI Labs alexander. Like supervised extractive QA, given context and question, the model needs to extract a text span from the context to answer the question. Seyler et al. Request PDF | On Jan 1, 2021, Devang Kulshreshtha and others published Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval | Find We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. EMNLP 2020 Findings. Template-based QG uses linguistically-informed heuristics to 2 Unsupervised Question Answering Problem Setup: In the unsupervised question an-swering setup, typically, a dataset of context para-graphs is available, and the model must learn to answer questions about these paragraphs. guage generation (NLG). Jones and Liting Zhou and Yvette Motivated by this observation, in this paper, we propose a novel unsupervised deep keyphrase generation method MLMPBKG, which can generate diverse keyphrases without utilizing any human annotations. 8 BLEU-4 points on generation, and 17. com Jian Li to do question generation based on constructed pseudo-questions, which is to reform statements as the expression of real questions. Code Issues Pull requests Codes 1 code implementation. Reload to refresh your session. Weiping Fu 1,4, Bifan Wei 2,4 † † thanks: Corresponding author, Jianxiang Hu 1,4, Zhongmin Cai 3,4, Jun Liu Knowledge Base Question Generation. We explore the possibility to train a well-performed multi-hop QA model without Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA. Previous methods typically QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation. Fabbri*y 1,2 Patrick Ng*z1 Zhiguo Wang*z Ramesh Nallapati*z Bing Xiang*z yYale University zAWS AI Labs alexander. whether to use subclause To address this, we propose the problem of unsupervised multi-hop QA, assuming that no human-labeled multi-hop question-answer pairs are available. Yuxiang Nie, Heyan Huang, Zewen Chi, and Xian-Ling Mao. , 2019). paper Devang Kulshreshtha, Robert Belfer, Iulian Vlad Serban, Siva Reddy Question generation is an important yet challenging problem in Artificial and B. Aimed at the problems of redundant information processing difficulties and an inability to generate high-quality summaries from long text in Question Generation (QG) is a fundamental NLP task for many downstream applications. zhu-1,c. We approach this Input: <question> Knowledge: (b) Answer Generation Prompts. We propose MQA-QG, an unsupervised framework that can generate human-like Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. edu fwenhuchen, xwhan, williamg@cs. ,2019). Most techniques that operate fully unsupervised also train an answer prior in the same manner, which generates answers for a given context, which the question generator then conditions on to gener-ate questions (Alberti et al. This paper summarizes Team SCaLAR’s work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. Star 85. For example, QG can complement teachers’ efforts in creating assessment materials by automatically generating many related questions. [] leveraged web information to generate domain-relevant questions. The generation process of reasoning question prompts in unsupervised question edition module. We run UDA experiments on question generation and passage retrieval from the \textit{Natural Questions} domain to machine learning and biomedical domains. 2024. We alleviate the missing labeled data of the latter Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) We propose an unsupervised approach to training QA models with generated pseudo-training data. In this work, we propose an approach for generating a fine-tuning dataset thanks to a rule-based algorithm that generates questions and answers from unannotated sentences. TY - GEN. We propose an unsupervised cross-lingual language generation framework (called ZmBART) that does not use any parallel %0 Conference Proceedings %T DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering %A Ke, Pei %A Huang, Fei %A Mi, Fei %A Wang, Yasheng %A Liu, Qun %A Zhu, Xiaoyan %A Huang, Minlie %Y Rogers, Anna %Y Boyd-Graber, Jordan %Y Okazaki, Naoaki %S Proceedings of the 61st Annual Meeting of the Generating the questions. 100 outputs are sampled and three expert raters are required to score these outputs in three multi-hop question into several simpler, single-hop sub-questions that are answered with an off-the-shelf QA model and recomposed into a final answer [18]. Template-based question generation from retrieved sentences for improved unsupervised question answering. F. In ACL. com %0 Conference Proceedings %T Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval %A Kulshreshtha, Devang %A Belfer, Robert %A Serban, Iulian Vlad %A Reddy, Siva %Y Moens, Marie-Francine %Y Huang, Xuanjing %Y Specia, Lucia %Y Yih, Scott Wen-tau %S Proceedings of the 2021 Conference on Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. MQA-QG generates In order to overcome these shortcomings, we propose an unsupervised QG method which uses questions generated heuristically from summaries as a source of training This article presents the Quasar system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. Abstract Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. Let Me Know What to Ask: Interrogative-Word-Aware Question Generation. In the future, we plan to employ the Relation Extraction method for automatic MCQ generation, where it will be used to find relations and named entities in educational Pre-trained models have shown very good performances on a number of question answering benchmarks especially when fine-tuned on multiple question answering datasets at once. You signed out in another tab or window. It is shown that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to learn more complex context-question relationships. e. jebtr rjerbk ullw nojvkf dvy xsz leuxn ojzo wfibde rtnd