Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering

Golestani, Melika; Razavi, Seyedeh Zahra; Borhanifard, Zeinab; Tahmasebian, Farnaz; Faili, Hesham

doi:10.1007/978-3-030-83527-9_27

Cited by 6 publications

(2 citation statements)

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“…(4) Most of the existing pretext tasks transfer well across modalities. For instance, Masked Language Modelling (MLM) in the text domain has been applied to audio and image, e.g., Masked Acoustic Modelling [180], [200], Masked Image Region Prediction [190], while both Sentence Ordering Modelling (SOM) [201] in text domain and Frame Ordering Modelling (FOM) [116] in video domain share the same idea. We will further discuss the pretext tasks for multimodal Transformer pretraining in the follows.…”

Section: Task-agnostic Multimodal Pretrainingmentioning

confidence: 99%

Multimodal Learning With Transformers: A Survey

Zhu

Clifton

2023

IEEE Trans. Pattern Anal. Mach. Intell.

218

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Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and big data, Transformer-based multimodal learning has become a hot topic in AI research. This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data. The main contents of this survey include: (1) a background of multimodal learning, Transformer ecosystem, and the multimodal big data era, (2) a systematic review of Vanilla Transformer, Vision Transformer, and multimodal Transformers, from a geometrically topological perspective, (3) a review of multimodal Transformer applications, via two important paradigms, i.e., for multimodal pretraining and for specific multimodal tasks, (4) a summary of the common challenges and designs shared by the multimodal Transformer models and applications, and (5) a discussion of open problems and potential research directions for the community.

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Section: Task-agnostic Multimodal Pretrainingmentioning

confidence: 99%

Multimodal Learning With Transformers: A Survey

Zhu

Clifton

2023

IEEE Trans. Pattern Anal. Mach. Intell.

218

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“…In this study, the encoding part has been used for text feature extraction in order to turn the sentence into its respective vector [57].…”

Section: The Second Strategy: Distilbert Language Model (Transformers...mentioning

confidence: 99%

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Shoeibi

Hernández

et al. 2021

Electronics

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Maintaining a healthy cyber society is a great challenge due to the users’ freedom of expression and behavior. This can be solved by monitoring and analyzing the users’ behavior and taking proper actions. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using graph analysis methods. Then, the users’ behavioral patterns are analyzed by applying metadata analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then, in the abnormal behavior detection and filtering component, the interesting profiles are selected for further examinations. Finally, in the contextual analysis component, the contents are analyzed using natural language processing techniques; a binary text classification model (SVM (Support Vector Machine) + TF-IDF (Term Frequency—Inverse Document Frequency) with 88.89% accuracy) is used to detect if a tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN (Feed-Forward Neural Network) with 80% accuracy), because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (the police) with suggestions to control hate speech or terrorist propaganda.

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