Papers by Keyi Tang
TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface (2021.acl-demo)
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Peng Xu, Wenjie Zi, Hamidreza Shahidi, Ákos Kádár, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, Yanshuai Cao
| Challenge: | Existing text-to-SQL semantic parsers cannot achieve high accuracy in cross-database setting . TURING is a NLDB system that can be used to democratize data-driven insights for non-technical users . |
| Approach: | They propose a TURING system that provides high-precision natural language explanations of SQL queries in a beam. |
| Outcome: | The proposed system achieves 75.1% execution accuracy and 78.3% top-5 beam execution accuracy on the Spider validation set. |
Optimizing Deeper Transformers on Small Datasets (2021.acl-long)
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Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao
| Challenge: | a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets. |
| Approach: | They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch. |
| Outcome: | The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch . |
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data (2021.acl-short)
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| Challenge: | a generic transformer-based model can achieve competitive performance with minimal code-generation-specific inductive bias design. |
| Approach: | They investigate whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design. |
| Outcome: | The proposed model achieves 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa. |