Search Results for author: Rishiraj Saha Roy

Found 24 papers, 12 papers with code

Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation

no code implementations20 Oct 2023 Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum

This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions.

Conversational Question Answering Knowledge Graphs

CompMix: A Benchmark for Heterogeneous Question Answering

no code implementations21 Jun 2023 Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum

Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources.

Question Answering

Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks

1 code implementation2 May 2023 Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum

In conversational question answering, users express their information needs through a series of utterances with incomplete context.

Conversational Question Answering

Conversational Question Answering on Heterogeneous Sources

no code implementations25 Apr 2022 Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum

Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit.

Conversational Question Answering

Complex Temporal Question Answering on Knowledge Graphs

1 code implementation18 Sep 2021 Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy, Gerhard Weikum

This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions.

Entity Embeddings Knowledge Graphs +1

UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text

1 code implementation19 Aug 2021 Soumajit Pramanik, Jesujoba Alabi, Rishiraj Saha Roy, Gerhard Weikum

Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone.

Knowledge Graphs Question Answering

Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases

1 code implementation19 Aug 2021 Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum

Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates.

Entity Disambiguation Knowledge Graphs +1

Counterfactual Explanations for Neural Recommenders

1 code implementation11 May 2021 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.

Collaborative Filtering counterfactual

Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs

1 code implementation11 May 2021 Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum

We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations.

Conversational Question Answering Knowledge Graphs +2

ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

1 code implementation15 Feb 2021 Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy, Gerhard Weikum

System-provided explanations for recommendations are an important component towards transparent and trustworthy AI.

Conversational Question Answering over Passages by Leveraging Word Proximity Networks

1 code implementation27 Apr 2020 Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum

In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns.

Conversational Question Answering Information Retrieval +2

Question Answering over Curated and Open Web Sources

no code implementations24 Apr 2020 Rishiraj Saha Roy, Avishek Anand

The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence.

Information Retrieval Knowledge Graphs +2

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

no code implementations4 Apr 2020 Asia J. Biega, Jana Schmidt, Rishiraj Saha Roy

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood.

Community Question Answering

CROWN: Conversational Passage Ranking by Reasoning over Word Networks

1 code implementation7 Nov 2019 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 understanding the conversational context of a request to retrieve correct answers.

Passage Ranking

TEQUILA: Temporal Question Answering over Knowledge Bases

no code implementations9 Aug 2019 Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Stroetgen, Gerhard Weikum

An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled.

Question Answering

FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds

1 code implementation8 Aug 2019 Azin Ghazimatin, Rishiraj Saha Roy, Gerhard Weikum

We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user.

Learning-To-Rank

ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters

no code implementations NAACL 2019 Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, Gerhard Weikum

To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated.

Question Answering

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