This thesis describes advancements in question answering along three general directions: model architecture extensions, explainable question answering, and data augmentation.
Chapter 2 describes the first state-of-the-art model for the Natural Questions dataset based on pretrained transformers. Chapters 3 and 4 describe extensions to the model architecture designed to accommodate long textual inputs and multimodal text+image inputs, establishing new state-of-the-art results on the Natural Questions and on the VCR dataset.
Chapter 5 shows that significant improvements can be obtained with data augmentation on the SQuAD and Natural Questions dataset, introducing roundtrip consistency as a simple heuristic to improve the quality of synthetic data. In Chapters 6 and 7 we explore explainable question answering, demonstrating the usefulness of a new concrete kind of structured explanations, QED, and proposing a semantic analysis of why-questions in the Natural Questions, as a way of better understanding the nature of real world explanations.
Finally, in Chapters 8 and 9 we delve into more exploratory data augmentation techniques for question answering. We look respectively at how straight-through gradients can be utilized to optimize roundtrip consistency in a pipeline of models on the fly, and at how very recent large language models like PaLM can be used to generate synthetic question answering datasets for new languages given as few as five representative examples per language.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/e6qn-5j96 |
| Date | January 2023 |
| Creators | Alberti, Christopher Brian |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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