1 code implementation • 28 Jan 2023 • Laura Perez-Beltrachini, Parag Jain, Emilio Monti, Mirella Lapata
In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).
no code implementations • 7 Sep 2021 • Parag Jain, Mirella Lapata
We present a memory-based model for context-dependent semantic parsing.
no code implementations • ACL 2019 • Abhijit Mishra, Anirban Laha, Karthik Sankaranarayanan, Parag Jain, Saravanan Krishnan
In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language.
1 code implementation • ACL 2019 • Priyanka Agrawal, Parag Jain, Ayushi Dalmia, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.
1 code implementation • ACL 2019 • Sai Surya, Abhijit Mishra, Anirban Laha, Parag Jain, Karthik Sankaranarayanan
The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.
Ranked #2 on Text Simplification on ASSET
1 code implementation • CL 2019 • Anirban Laha, Parag Jain, Abhijit Mishra, Karthik Sankaranarayanan
We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG).
1 code implementation • 10 Sep 2018 • Parag Jain, Abhijit Mishra, Amar Prakash Azad, Karthik Sankaranarayanan
We propose a novel framework for controllable natural language transformation.
2 code implementations • NAACL 2018 • Preksha Nema, Shreyas Shetty, Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Mitesh M. Khapra
For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level).
1 code implementation • NAACL 2018 • Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, Shreyas Shetty
Structured data summarization involves generation of natural language summaries from structured input data.
no code implementations • 18 Jul 2017 • Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, Karthik Sankaranarayanan
Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity.
no code implementations • 15 Mar 2016 • Ramandeep S Randhawa, Parag Jain, Gagan Madan
We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings.