Papers by Jack Rae
Do Transformers Need Deep Long-Range Memory? (2020.acl-main)
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| Challenge: | Deep attention models have advanced the modelling of sequential data across many domains. |
| Approach: | They propose to use a Transformer augmented with a long-range memory to model sequential data across many domains. |
| Outcome: | The Transformer-XL has a long-range memory at every layer of the network, rendering its state thousands of times larger than RNN predecessors. |
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2020.findings-emnlp)
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Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli
| Challenge: | Language modeling has advanced rapidly due to efficient model architectures and the availability of large text corpora. |
| Approach: | They propose to embed and regularize sentiment prediction-derived regularizations on the language model’s latent representations to reduce bias in the sentiment of generated text. |
| Outcome: | The proposed methods reduce bias in the sentiment of generated text by adopting individual and group fairness metrics from the fair machine learning literature. |