Search Results for author: Debanjan Chaudhuri

Found 10 papers, 9 papers with code

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings

no code implementations25 Feb 2020 Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann, Asja Fischer

Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs.

Entity Disambiguation Entity Linking +4

Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning

1 code implementation28 Jan 2020 Firas Kassawat, Debanjan Chaudhuri, Jens Lehmann

Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input.

Goal-Oriented Dialog Goal-Oriented Dialogue Systems +2

Using a KG-Copy Network for Non-Goal Oriented Dialogues

1 code implementation17 Oct 2019 Debanjan Chaudhuri, Md Rashad Al Hasan Rony, Simon Jordan, Jens Lehmann

Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts.

Knowledge Graphs Response Generation

A Retrospective Analysis of the Fake News Challenge Stance-Detection Task

1 code implementation COLING 2018 Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych

To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.

General Classification Stance Classification +1

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

7 code implementations13 Jun 2018 Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych

To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.

General Classification Stance Classification +1

EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

1 code implementation11 Jan 2018 Mohnish Dubey, Debayan Banerjee, Debanjan Chaudhuri, Jens Lehmann

Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph.

Entity Linking Knowledge Graphs +3

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