Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data Transformations

by   Charaf Eddine Benarab, et al.

Knowledge is acquired by humans through experience, and no boundary is set between the kinds of knowledge or skill levels we can achieve on different tasks at the same time. When it comes to Neural Networks, that is not the case, the major breakthroughs in the field are extremely task and domain specific. Vision and language are dealt with in separate manners, using separate methods and different datasets. In this work, we propose to use knowledge acquired by benchmark Vision Models which are trained on ImageNet to help a much smaller architecture learn to classify text. After transforming the textual data contained in the IMDB dataset to gray scale images. An analysis of different domains and the Transfer Learning method is carried out. Despite the challenge posed by the very different datasets, promising results are achieved. The main contribution of this work is a novel approach which links large pretrained models on both language and vision to achieve state-of-the-art results in different sub-fields from the original task. Without needing high compute capacity resources. Specifically, Sentiment Analysis is achieved after transferring knowledge between vision and language models. BERT embeddings are transformed into grayscale images, these images are then used as training examples for pretrained vision models such as VGG16 and ResNet Index Terms: Natural language, Vision, BERT, Transfer Learning, CNN, Domain Adaptation.


page 2

page 3

page 4


FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Financial sentiment analysis is a challenging task due to the specialize...

Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya

In recent years, transformer models have achieved great success in natur...

Characterization of effects of transfer learning across domains and languages

With ever-expanding datasets of domains, tasks and languages, transfer l...

Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences

Domain adaptation or transfer learning using pre-trained language models...

BERT for Sentiment Analysis: Pre-trained and Fine-Tuned Alternatives

BERT has revolutionized the NLP field by enabling transfer learning with...

Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis

Multimodal learning pipelines have benefited from the success of pretrai...

Dropping Networks for Transfer Learning

In natural language understanding, many challenges require learning rela...

Please sign up or login with your details

Forgot password? Click here to reset