Natural Language Understanding and Large-Scale Pretrained Language Models for Enhancing Multilingual Translation, Sentiment Analysis, and Conversational AI
Keywords:
Natural Language Understanding, Pretrained Language Models, Multilingual Translation, Sentiment Analysis, Conversational AI, Deep LearningAbstract
Natural Language Understanding (NLU) has witnessed significant advancements with the emergence of large-scale pretrained language models (PLMs). These models have enhanced multilingual translation, sentiment analysis, and conversational AI by improving contextual understanding, disambiguation, and syntactic coherence. This paper explores the development of NLU, highlighting the role of PLMs such as BERT, GPT, and T5. We conduct a literature review of key studies before 2023 and analyze performance metrics, advancements, and challenges in multilingual AI. Empirical results demonstrate the superior capabilities of PLMs over traditional approaches. The findings suggest that while pretrained models exhibit robust linguistic capabilities, challenges related to bias, interpretability, and computational costs persist.
References
Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." arXiv preprint arXiv:1810.04805, 2018.
Brown, Tom, et al. "Language Models Are Few-Shot Learners." NeurIPS, 2020.
Vaswani, Ashish, et al. "Attention is All You Need." NeurIPS, 2017.
Raffel, Colin, et al. "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer." JMLR, 2020.
Conneau, Alexis, et al. "Unsupervised Cross-lingual Representation Learning at Scale." arXiv preprint arXiv:1911.02116, 2019.
Radford, Alec, et al. "Improving Language Understanding by Generative Pre-training." OpenAI Report, 2018.
Touvron, Hugo, et al. "LLaMA: Open and Efficient Foundation Language Models." arXiv preprint arXiv:2302.13971, 2023.
Lample, Guillaume, and Alexis Conneau. "Cross-lingual Language Model Pretraining." NeurIPS, 2019.
Bommasani, Rishi, et al. "On the Opportunities and Risks of Foundation Models." arXiv preprint arXiv:2108.07258, 2021.
Liu, Yinhan, et al. "RoBERTa: A Robustly Optimized BERT Pretraining Approach." arXiv preprint arXiv:1907.11692, 2019.
Wu, Yonghui, et al. "Google's Neural Machine Translation System." arXiv preprint arXiv:1609.08144, 2016.
Zhang, Tianyi, et al. "BERTScore: Evaluating Text Generation with BERT." ICLR, 2020.
Shoeybi, M., et al. "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism." arXiv preprint arXiv:1909.08053, 2019.
Goyal, Naman, et al. "The FLORES Evaluation Benchmark for Low-Resource and Multilingual Machine Translation." arXiv preprint arXiv:2106.03193, 2021.
Wei, Jason, et al. "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." arXiv preprint arXiv:2201.11903, 2022.
