Deep Learning in Question Answering

Liu, Kang; Feng, Yansong

doi:10.1007/978-981-10-5209-5_7

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Academics and researchers in several domains, including computer vision theory [36,37], healthcare [38], and others, have been interested in Deep learning [43,44], which does not necessitate costly, manually crafted feature engineering like other ML approaches. Deep learning uses simulated neural networks that mimic brain activity.…”

Section: B Deep Learningmentioning

confidence: 99%

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Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges

Nadella

2024

IJRASET

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Edge computation (EC) represents a transformative architecture in which cloud computing services are decentralized to the locations where data originates. This shift has been facilitated by the integration of deep learning (DL) technologies, notably in eliminating latency issues commonly referred to as the "echo effect" across various platforms. In typical EC-enabled DL frameworks where data producers are directly involved, it is often necessary to share data with third parties or edge/cloud servers to facilitate model training. This process, however, raises significant concerns regarding synchronization with high data rates, seamless migration, and security, consequently exposing the system to privacy vulnerabilities. These challenges can be addressed through the adoption of Federated Learning (FL), which provides a robust mechanism to mitigate risks associated with data loss, ensure data freshness, and enhance privacy.

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Section: B Deep Learningmentioning

confidence: 99%

“…There is a host of malware and anomaly detection, and steps to counter it from the other environments have been proposed. A deep learning-aided cyber pervasive-attack detection approach [43] was proposed as per the Gaussian naive Bayes classifier model.…”

Section: Malware and Anomaly Detectionmentioning

confidence: 99%

Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges

Nadella

2024

IJRASET

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“…In recent years, publicly available knowledge graphs (e.g., DBpedia [21], Wikidata [40]) and Yago [36]) have been widely used as a source of knowledge in several tasks such as entity linking, relation extraction, and question answering [22]. Question answering (QA) over knowledge graphs, in particular, is an essential task that maps a user's utterance to a query over a knowledge graph (KG) to retrieve the correct answer [34].…”

Section: Introductionmentioning

confidence: 99%

ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

Kacupaj¹,

Banerjee²,

Singh³

et al. 2021

Preprint

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This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG). The dataset was created using a semiautomated framework for generating diverse paraphrasing of the answers using techniques such as back-translation. The existing datasets for conversational question answering over KGs (single-turn/multi-turn) focus on question paraphrasing and provide only up to one answer verbalization. However, ParaQA contains 5000 question-answer pairs with a minimum of two and a maximum of eight unique paraphrased responses for each question. We complement the dataset with baseline models and illustrate the advantage of having multiple paraphrased answers through commonly used metrics such as BLEU and METEOR. The ParaQA dataset is publicly available on a persistent URI for broader usage and adaptation in the research community.

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ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

Kacupaj

Banerjee

Singh³

et al. 2021

Lecture Notes in Computer Science

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Deep Learning in Question Answering

Cited by 7 publications

References 29 publications

Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges

Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges

ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

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