Papers by Andrew Mattarella-Micke
Do Long-Range Language Models Actually Use Long-Range Context? (2021.emnlp-main)
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| Challenge: | Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. |
| Approach: | They analyze two long-range Transformer language models that accept 8K token inputs . they find that providing long-term context only improves their predictions on a small set of tokens - not sentence-level ones . |
| Outcome: | The proposed model improves on PG-19 with only 2K tokens and does not help at all for sentence-level prediction tasks. |
Exploring and Predicting Transferability across NLP Tasks (2020.emnlp-main)
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Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
| Challenge: | Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. |
| Approach: | They conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems. |
| Outcome: | The proposed model can improve performance even with low-data source tasks that differ substantially from the target task. |