12 citations · 19 across the 5 of their papers we have counts for
8 papers
RoMe: A Robust Metric for Evaluating Natural Language Generation
Md Rashad Al Hasan Rony, Liubov Kovriguina, Debanjan Chaudhuri +2
Evaluating Natural Language Generation (NLG) systems is a challenging task. Firstly, the metric should ensure that the generated hypothesis reflects the reference's semantics. Seco…
Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
Debanjan Chaudhuri, Md Rashad Al Hasan Rony, Jens Lehmann
Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge. Knowledge Graphs (KG) can be viewed as an abstractio…
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
Debayan Banerjee, Debanjan Chaudhuri, Mohnish Dubey +1
Question Answering systems are generally modelled as a pipeline consisting of a sequence of steps. In such a pipeline, Entity Linking (EL) is often the first step. Several EL model…
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann +1
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on…
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
Firas Kassawat, Debanjan Chaudhuri, Jens Lehmann
Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and inc…
Using a KG-Copy Network for Non-Goal Oriented Dialogues
Debanjan Chaudhuri, Md Rashad Al Hasan Rony, Simon Jordan +1
Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consis…