81 citations · 215 across the 11 of their papers we have counts for
12 papers · 1 filter
CompMix: A Benchmark for Heterogeneous Question Answering
Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a tex…
Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks
Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (…
Complex Temporal Question Answering on Knowledge Graphs
Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy +1
Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much atte…
Counterfactual Explanations for Neural Recommenders
Khanh Hiep Tran, Azin Ghazimatin, Rishiraj Saha Roy
Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-…
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs
Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over k…
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models
Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy +1
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though,…