Papers by Christopher Richardson
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
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Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction (2021.naacl-main)
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Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah
| Challenge: | Existing systems frame semantic parsing as a one-shot translation from a natural language question to the logical form. |
| Approach: | They propose a model that uses natural language feedback to correct parsers . they show that NL-EDIT can boost the accuracy of existing parser by 20% . |
| Outcome: | The proposed model can boost parsers' accuracy by 20% with just one turn of correction. |
Structure-Grounded Pretraining for Text-to-SQL (2021.naacl-main)
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Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, Matthew Richardson
| Challenge: | STRUG is a weakly supervised structure-based pretraining framework for text-to-SQL . it can be used to learn to capture text-table alignment in a given database schema . |
| Approach: | They propose a weakly supervised structure-grounded pretraining framework for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-tab corpus. |
| Outcome: | The proposed framework outperforms BERTLARGE and BERTLAGE on all text-to-SQL alignment settings. |