Papers › Modern Methods for Text Generation

Modern Methods for Text Generation

10 Sep 2020arXiv:2009.04968archive 2025-07-28

Dimas Munoz Montesinos

Synthetic text generation is challenging and has limited success. Recently, a new architecture, called Transformers, allow machine learning models to understand better sequential data, such as translation or summarization. BERT and GPT-2, using Transformers in their cores, have shown a great performance in tasks such as text classification, translation and NLI tasks. In this article, we analyse both algorithms and compare their output quality in text generation tasks.

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DimasDMM/nlp-completer mentioned on GitHubpytorch report

DimasDMM/transformers mentioned on GitHubpytorch report

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BIG-bench Machine Learning Text Classification Text Generation Translation text-classification

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Methods

Adam Attention Attention Dropout BERT BPE Cosine Annealing Dense Connections Discriminative Fine-Tuning Dropout GPT-2 Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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