Papers by Sam Witteveen
Paraphrasing with Large Language Models (D19-56)
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| Challenge: | Recent work has shown large language models are adept at text generation and fine-tuning for downstream NLP tasks. |
| Approach: | They propose a system that generates paraphrased examples in autoregressive fashion using a neural network without the need for techniques such as top-k word selection or beam search. |
| Outcome: | The proposed system generates paraphrased examples in autoregressive fashion without the need for techniques such as top-k word selection or beam search. |
Unsupervised Natural Question Answering with a Small Model (D19-66)
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| Challenge: | a recent demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is embedded directly within these large models. |
| Approach: | They propose to use unsupervised learning techniques to add knowledge explicitly without extensive training. |
| Outcome: | The proposed architecture allows for explicit addition of knowledge without extensive training. |
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation (D19-53)
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| Challenge: | The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. |
| Approach: | The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. |
| Outcome: | The Explanation Regeneration shared task asked participants to develop methods to reconstruct gold explanations for elementary science questions. |