Exploring NLP and Information Extraction to Jointly Address Question Generation and Answering

Azevedo, Pedro; Leite, Bernardo; Cardoso, Henrique Lopes; Silva, Daniel Castro; Reis, Luís Paulo

doi:10.1007/978-3-030-49186-4_33

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Fluency (Fy), and 3. Grammaticality (Gy) categories, following Harrison and Walker (2018); Azevedo et al (2020). We include these three criteria as questions may have high Fluency and Grammaticality scores, but not be answerable.…”

Section: Results and Analysismentioning

confidence: 99%

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Ghanem¹,

Lutz²,

Rivard³

et al. 2022

Preprint

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Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating literal questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.

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Section: Results and Analysismentioning

confidence: 99%

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Ghanem¹,

Lutz²,

Rivard³

et al. 2022

Preprint

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show abstract

“…Document classification has become another essential research area where word embedding techniques are applied for semantic relationships between words [15]. Systems like informative dialogue flow [2,21] and chatbot [8] also incorporate word embedding techniques. There are lots of word embedding strategies accessible for popular dialects.…”

Section: Introductionmentioning

confidence: 99%

BnVec: Towards the Development of Word Embedding for Bangla Language Processing

Kowsher

Uddin²,

Tahabilder³

et al. 2021

IJET

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Progression in machine learning and statistical inference are facilitating the advancement of domains like computer vision, natural language processing (NLP), automation & robotics, and so on. Among the different persuasive improvements in NLP, word embedding is one of the most used and revolutionary techniques. In this paper, we manifest an open-source library for Bangla word extraction systems named BnVec which expects to furnish the Bangla NLP research community by the utilization of some incredible word embedding techniques. The BnVec is splitted up into two parts, the first one is the Bangla suitable defined class to embed words with access to the six most popular word embedding schemes (CountVectorizer, TF-IDF, Hash Vectorizer, Word2vec, fastText, and Glove). The other one is based on the pre-trained distributed word embedding system of Word2vec, fastText, and GloVe. The pre-trained models have been built by collecting content from the newspaper, social media, and Bangla wiki articles. The total number of tokens used to build the models exceeds 395,289,960. The paper additionally depicts the performance of these models by various hyper-parameter tuning and then analyzes the results.

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Machine Learning Adversarial Attacks: A Survey Beyond

Magoo¹,

Garg²

2021

Machine Learning Techniques and Analytics for Cloud Security

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Exploring NLP and Information Extraction to Jointly Address Question Generation and Answering

Cited by 7 publications

References 20 publications

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

BnVec: Towards the Development of Word Embedding for Bangla Language Processing

Machine Learning Adversarial Attacks: A Survey Beyond

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