Papers by Feifei Pan
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering (2023.acl-long)
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Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan
| Challenge: | Existing question answering systems for tables and linked text are relatively unexplored. |
| Approach: | They propose a transformer-based question answering system that copes with distant supervision along both axes of the question and answer. |
| Outcome: | The proposed system beats baselines for HybridQA and OTT-QA with best EM and F1 scores on a held out test set. |
CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering (2021.acl-demo)
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| Challenge: | Existing systems that retrieve tables based on keyword queries and table contents often result in poor quality . a growing demand for natural language questions over tables to be used for QA . |
| Approach: | They propose an end-to-end transformer-based table question answering system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables. |
| Outcome: | The proposed system can retrieve relevant tables and locate the correct cells to answer questions. |
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables (2021.naacl-main)
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Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia
| Challenge: | Existing transformer based approaches have been used to answer questions over tables. |
| Approach: | They propose a transformer based architecture that independently classifies rows and columns to identify relevant cells and a model that incorporates existing tables to improve efficiency. |
| Outcome: | The proposed model outperforms the state-of-the-art transformer based approaches on WikiSQL lookup questions and achieves 3.4% and 18.86% additional precision improvement on the standard WikisQL benchmark. |