While pretrained encoders have achieved success in various natural language understanding (NLU) tasks, there is a gap between these pretrained encoders and natural language generation (NLG). NLG tasks ...

We used a GRU-based unidirectional RNN model with an encoder-decoder-attention for this research. The research is conducted on a small-size balanced dataset. We have created a dataset of 1,014 ...

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

Natural Language Processing has many interesting applications and Sequence to Sequence modelling is one of those interesting applications. It has major applications in question-answering systems and ...

Smooth language translation is becoming more and more important in today's globalized society as it promotes efficient communication, knowledge sharing, and intercultural understanding. The study ...

This project was made by utilizing a dataset of TED talks translated from English to French for the purpose of creating a neural network that can generalize well enough to accurately predict the ...

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