Papers by Luke Melas-Kyriazi
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (2022.emnlp-main)
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| Challenge: | a new method for textual style transfer is proposed for text with a limited set of style choices . textual styles are a complex task that requires specialized models to perform . |
| Approach: | They propose a method for arbitrary textual style transfer using pre-trained language models . they use a mathematical formulation of the TST task, decomposing it into three components . |
| Outcome: | The proposed method performs on par with state-of-the-art large-scale models while using less compute and memory. |
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings (D19-61)
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| Challenge: | Recent research points to knowledge distillation as a potential solution for NLU tasks. |
| Approach: | They propose a training approach that distills large finetuned LMs into a small network using unlabeled training examples. |
| Outcome: | The proposed approach outperforms BERT training approaches while using 300 times fewer parameters. |
Training for Diversity in Image Paragraph Captioning (D18-1)
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| Challenge: | Existing image captioning models have a lack of diversity between sentences . current models have limited their effectiveness due to repetitive paragraphs . |
| Approach: | They propose to apply sequence-level training to image paragraph captioning models . they find that standard self-critical training produces poor results . |
| Outcome: | The proposed training improves on the Visual Genome dataset with no architectural changes. |
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding (2023.findings-acl)
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| Challenge: | Existing text decoding methods struggle to produce high-quality text . Greedy and beam search suffer from text degeneration and linguistic diversity issues . |
| Approach: | They propose a family of decoding methods based on minimum bayes risk minimization to address diversity-quality trade-offs in open-ended natural-language generation. |
| Outcome: | The proposed methods improve diversity-quality trade-offs on open-ended natural-language generation tasks. |