Papers with pretrained transformer language models
Efficient Transformer Knowledge Distillation: A Performance Review (2023.emnlp-industry)
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| Challenge: | Pretrained transformer language models have been gaining popularity in the field of natural language processing . however, there is no study into the intersection of these two fields . |
| Approach: | They propose a method to extract knowledge from transformers to produce high-performing efficient attention models with low costs. |
| Outcome: | The proposed model compression method preserves up to 98.6% of original model performance across short-context tasks and up to 95.8% on long-concept Named Entity Recognition tasks while decreasing inference times by up to 57%. |
Byte Pair Encoding is Suboptimal for Language Model Pretraining (2020.findings-emnlp)
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| Challenge: | Subword tokenization is a popular language model that can be used to segment text. |
| Approach: | They analyze differences between byte-pair encoding (BPE) and unigram LM tokenization methods to find subword units that align more closely with morphology. |
| Outcome: | The proposed method recovers subword units that align more closely with morphology and avoids problems stemming from BPE’s greedy construction procedure. |