1 code implementation • 13 Oct 2022 • Mayank Mishra, Danish Contractor, Dinesh Raghu
Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses.
1 code implementation • 24 Feb 2022 • Kevin Leyton-Brown, Mausam, Yatin Nandwani, Hedayat Zarkoob, Chris Cameron, Neil Newman, Dinesh Raghu
Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper.
1 code implementation • EMNLP 2021 • Dinesh Raghu, Shantanu Agarwal, Sachindra Joshi, Mausam
We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e. g., car not starting).
no code implementations • Findings (ACL) 2021 • Dinesh Raghu, Atishya Jain, Mausam, Sachindra Joshi
In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record.
no code implementations • 30 Apr 2020 • Dinesh Raghu, Nikhil Gupta, Mausam
Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses.
no code implementations • 11 Feb 2020 • Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts.
no code implementations • 2 Nov 2018 • Dhiraj Madan, Dinesh Raghu, Gaurav Pandey, Sachindra Joshi
However these states need to be handcrafted and annotated in the data.
1 code implementation • NAACL 2019 • Revanth Reddy, Danish Contractor, Dinesh Raghu, Sachindra Joshi
Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results.
1 code implementation • NAACL 2019 • Dinesh Raghu, Nikhil Gupta, Mausam
We also systematically modify existing datasets to measure disentanglement and show BoSsNet to be robust to KB modifications.
no code implementations • EACL 2017 • Sathish Reddy, Dinesh Raghu, Mitesh M. Khapra, Sachindra Joshi
To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph.