6 citations · 7 across the 3 of their papers we have counts for
5 papers
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber +1
Open-domain question answering answers a question based on evidence retrieved from a large corpus. State-of-the-art neural approaches require intermediate evidence annotations for…
What's in a Name? Answer Equivalence For Open-Domain Question Answering
Chenglei Si, Chen Zhao, Jordan Boyd-Graber
A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are consid…
Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber +1
Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces. Current approaches incorporate the strengths of structured knowledge…
Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy
Maharshi Gor, Kellie Webster, Jordan Boyd-Graber
The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, mod…
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries
Tianze Shi, Chen Zhao, Jordan Boyd-Graber +2
Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the u…