Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

Huang, Zhen; Xu, Shiyi; Hu, Minghao; Wang, Xinyi; Qiu, Jinyan; Fu, Yongquan; Zhao, Yuehong; Peng, Yuxing; Wang, Changjian

doi:10.1109/access.2020.2988903

Cited by 45 publications

(18 citation statements)

References 92 publications

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“…Since the original RNNs are unable to learn the dependency found in input data especially when the gap is large, LSTM, due to the proposed gate functions, could handle such a problem well [20]. In practice, the powerful learning capacity of the LSTM method makes it one of the most used DL architectures and has been widely used in many fields, such as sentiment analysis [15,21,22], question answering systems [23], sentence embedding [24], and text classification [25].…”

Section: Long Short-term Memorymentioning

confidence: 99%

“…The skip-gram model computes the conditional probability of a word by predicting the surrounding context words given the central target word. The CBOW does the opposite of skip-gram, by computing the conditional probability of a target word give the context words surrounding it across a window of size k [23]. Mathematically, both CBOW (Equation ( 1)) and skip-gram (Equation ( 2)) models are trained as follows:…”

Section: Word Embeddingsmentioning

confidence: 99%

“…The pre-trained word2vec word embeddings have 300 features which trained on 100 billion words. − GloVe is an unsupervised training "count-based" model [23]. Opposite to word2vec, the GloVe word embedding generates the embedding vector using word occurrences.…”

Section: Word Embeddingsmentioning

confidence: 99%

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A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

et al. 2021

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Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.

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Section: Long Short-term Memorymentioning

confidence: 99%

Section: Word Embeddingsmentioning

confidence: 99%

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A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

et al. 2021

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“…The third challenge is to gather responses from multiple sources to compose an answer. As in document retrieval, multiple neural ranking models are proposed to retrieve answers that are relevant to a given user's question (Guo et al, 2019;Abbasiyantaeb & Momtazi, 2020;Huang et al, 2020). The neural ranking models for QA cover all five proposed categories for document retrieval with a focus on the semantic matching signal between questions and answers.…”

Section: Question-answeringmentioning

confidence: 99%

Neural ranking models for document retrieval

et al. 2021

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Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.

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“…Entity Search and Question Answering. Entity-centric search and question answering are broad areas that cover a variety of information-seeking needs, see surveys like [2,9,18,28]. As far as quantities are concerned, lookups are supported by many methods, over both knowledge graphs and text documents, and are part of major benchmarks, such as QALD [36], NaturalQuestions [22], ComplexWebQuestions [35], LC-QuAD [12] and others.…”

Section: Related Workmentioning

confidence: 99%

Extracting Contextualized Quantity Facts from Web Tables

Pal

Razniewski

et al. 2021

Proceedings of the Web Conference 2021

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Quantity queries, with filter conditions on quantitative measures of entities, are beyond the functionality of search engines and QA assistants. To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. This involves recognizing quantities, with normalized values and units, aligning them with the proper entities, and contextualizing these pairs with informative cues to match sophisticated queries with modifiers. Our method includes a new approach to aligning quantity columns to entity columns. Prior works assumed a single subject-column per table, whereas our approach is geared for complex tables and leverages external corpora as evidence. For contextualization, we identify informative cues from text and structural markup that surrounds a table. For querytime fact ranking, we devise a new scoring technique that exploits both context similarity and inter-fact consistency. Comparisons of our building blocks against state-of-the-art baselines and extrinsic experiments with two query benchmarks demonstrate the benefits of our method.

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Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

Cited by 45 publications

References 92 publications

A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

Neural ranking models for document retrieval

Extracting Contextualized Quantity Facts from Web Tables

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