45 citations · 48 across the 6 of their papers we have counts for
6 papers
Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering
Magdalena Kaiser, Gerhard Weikum
Conversational Question Answering (ConvQA) involves multiple subtasks, i) to understand incomplete questions in their context, ii) to retrieve relevant information, and iii) to gen…
Learning from Relevant Subgoals in Successful Dialogs using Iterative Training for Task-oriented Dialog Systems
Magdalena Kaiser, Patrick Ernst, György Szarvas
Task-oriented Dialog (ToD) systems have to solve multiple subgoals to accomplish user goals, whereas feedback is often obtained only at the end of the dialog. In this work, we prop…
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation
Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limit…
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…
Conversational Question Answering over Passages by Leveraging Word Proximity Networks
Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a us…
CROWN: Conversational Passage Ranking by Reasoning over Word Networks
Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understan…