Results 11 to 20 of about 1,082 (177)
Engineering ReportsAs the frequency of information updates in the financial market continues to rise, the traditional stock forecasting methods that rely only on historical prices and macro indicators have been difficult to fully describe the nonlinear impact of news ...
Jingyu Zhang
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Leveraging BERT, DistilBERT, and TinyBERT for Rumor Detection
IEEE AccessThe rapid spread of false information on social media has become a major challenge in today’s digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time.
Aijazahamed Qazi +4 more
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A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT
IEEE Access, 2023 While user-oriented service industries are rapidly growing, various network devices provide these services through different access paths. Accordingly, the network flow is also increasing explosively.
Chang-Yui Shin +3 more
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Understanding Perceived Quality of Mobile Apps Using DistilBERT Model
IEEE AccessMeasuring the perceived quality of mobile applications is a pivotal consideration for app developers, users, and platforms, as perceived quality can be measured by getting user reviews.
Amanullah +3 more
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CoRR, 2022 Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput to comply with certain latency constraints that prevents Transformer models from being used in production.
Haihao Shen +9 more
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BERTino: an Italian DistilBERT model [PDF]
, 2020 1The recent introduction of Transformers language representation models allowed great improvements in many natural language processing (NLP) tasks. However, if on one hand the performances achieved by this kind of architectures are surprising, on the other their usability is limited by the high number of parameters which constitute their network ...
Matteo Muffo, Enrico Bertino
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An Investigation of Structures Responsible for Gender Bias in BERT and DistilBERT
, 2023 In recent years, large Transformer-based Pre-trained Language Models (PLM) have changed the Natural Language Processing (NLP) landscape, by pushing the performance boundaries of the state-of-the-art on a wide variety of tasks. However, this performance gain goes along with an increase in complexity, and as a result, the size of such models (up to ...
Thibaud Leteno +3 more
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Multilingual Fine-Grained Named Entity Recognition [PDF]
Computer Science Journal of Moldova, 2023 The “MultiCoNER II Multilingual Complex Named Entity Recognition” task\footnote[1]{\url{https://multiconer.github.io}} within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs,
Viorica-Camelia Lupancu, Adrian Iftene
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A PERFORMANCE ANALYSIS OF SUGGESTION MINING IN MOBILE APP REVIEWS USING LARGE LANGUAGE MODELS TRANSFER LEARNING TECHNIQUES [PDF]
Proceedings on Engineering SciencesOne of the enduring issues with obtaining user feedback is to find effective ways of actionable improvement recommendations from this feedback for quality mobile app development.
Makarand Lotan Mali , Nitin N. Patil
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Data Augmentation Methods for Enhancing Robustness in Text Classification Tasks
Algorithms, 2023 Text classification is widely studied in natural language processing (NLP). Deep learning models, including large pre-trained models like BERT and DistilBERT, have achieved impressive results in text classification tasks.
Huidong Tang +2 more
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