Papers by Suyi Li
SParC: Cross-Domain Semantic Parsing in Context (P19-1)
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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences. |
| Approach: | They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences. |
| Outcome: | The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time. |
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
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| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
| Approach: | They propose a model which integrates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. |
| Outcome: | The proposed model achieves competitive results on large-scale datasets. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |