Non-Contextual BERT or FastText? A Comparative Analysis

• URL: http://arxiv.org/abs/2411.17661v3
• Date: Thu, 20 Feb 2025 14:41:23 GMT
• Title: Non-Contextual BERT or FastText? A Comparative Analysis
• Authors: Abhay Shanbhag, Suramya Jadhav, Amogh Thakurdesai, Ridhima Sinare, Raviraj Joshi,
• Abstract summary: We analyze the effectiveness of non-contextual embeddings from BERT models and FastText models for tasks such as news classification, sentiment analysis, and hate speech detection.<n>Our findings indicate that non-contextual BERT embeddings extracted from the model's first embedding layer outperform FastText embeddings.
• Score: 0.4194295877935868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural Language Processing (NLP) for low-resource languages, which lack large annotated datasets, faces significant challenges due to limited high-quality data and linguistic resources. The selection of embeddings plays a critical role in achieving strong performance in NLP tasks. While contextual BERT embeddings require a full forward pass, non-contextual BERT embeddings rely only on table lookup. Existing research has primarily focused on contextual BERT embeddings, leaving non-contextual embeddings largely unexplored. In this study, we analyze the effectiveness of non-contextual embeddings from BERT models (MuRIL and MahaBERT) and FastText models (IndicFT and MahaFT) for tasks such as news classification, sentiment analysis, and hate speech detection in one such low-resource language Marathi. We compare these embeddings with their contextual and compressed variants. Our findings indicate that non-contextual BERT embeddings extracted from the model's first embedding layer outperform FastText embeddings, presenting a promising alternative for low-resource NLP.

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