Natural Language Generation: Understanding the Task and Techniques

This lecture introduces the concept of Natural Language Generation (NLG), a crucial sub-field of natural language processing focused on creating coherent and useful text for human consumption. The instructor discusses the importance of building better benchmarks and highlights the challenges posed by annotator bias in datasets. Various strategies to mitigate these biases are presented, including manual rebalancing of datasets and the use of adversarial filtering algorithms. The lecture also covers the significance of data augmentation and intentional design in constructing controlled datasets for evaluation. The instructor emphasizes the role of autoregressive models in text generation, explaining how they predict the next token based on previous tokens. The lecture concludes with a discussion on decoding methods, including the limitations of greedy algorithms and the advantages of beam search for generating more coherent sequences. Overall, the lecture provides a comprehensive overview of the tasks and methodologies involved in NLG, setting the stage for deeper exploration in subsequent sessions.