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Text simplification can be framed as a sequence-to-sequence learning task. In this approach, an encoder-decoder model is trained on a dataset of corresponding original and simplified text pairs. The model learns to transform a complex input sequence into a simpler output sequence, effectively automating the simplification process.

Sequence-to-Sequence Models for Text Simplification

Based on the case study provided, identify the most significant flaw in the development team's approach and justify why this flaw led to the observed poor performance. Then, propose a specific, actionable change to their training process to address this issue.

Evaluating a Text Simplification Model's Performance

You are tasked with developing a model to automatically simplify technical documentation for a general audience. Arrange the following key stages of this project into the correct logical sequence.

A research team is developing a model to automatically simplify complex legal documents into plain language. They decide to frame this as a sequence-to-sequence learning problem using an encoder-decoder architecture. Which of the following is the most critical requirement for training this model to perform the simplification task effectively?

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