Search Results for author:
Found 6 papers, 3 papers with code
Faithful Temporal Question Answering over Heterogeneous Sources
The method has three stages: (i) understanding the question and its temporal conditions, (ii) retrieving evidence from all sources, and (iii) faithfully answering the question.
CompMix: A Benchmark for Heterogeneous Question Answering
Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources.
Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks
In conversational question answering, users express their information needs through a series of utterances with incomplete context.
Conversational Question Answering on Heterogeneous Sources
Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit.
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases
Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates.
Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion
Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic.
