Papers by Xiyou Zhou
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020.findings-emnlp)
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| Challenge: | Recent studies on Natural Language Generation (NLG) from structured data focus on surface descriptions of simple record sequences, for example, attribute-value pairs of fixed or very limited schema. |
| Approach: | They propose to use a large-scale dataset to generate NLG from logical forms to obtain controllable and faithful generations from structured data. |
| Outcome: | The proposed model can describe interesting facts from logical inferences across records, but it is difficult to produce such fidelity. |
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing (2021.eacl-demos)
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| Challenge: | Pretrained models have been taking the lead of many natural language processing benchmarks such as GLUE, but energy efficiency in the process of model training and inference becomes a critical bottleneck. |
| Approach: | They propose a multi-task energy efficiency benchmarking platform for responsible natural language processing that compares pretrained models’ energy efficiency from the perspectives of time and cost. |
| Outcome: | The proposed model improves on the fine-tuning efficiency of pretrained models from the perspectives of time and cost. |