Search Results for author: Parag Jain

Found 11 papers, 7 papers with code

Semantic Parsing for Conversational Question Answering over Knowledge Graphs

1 code implementation28 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).

Conversational Question Answering Knowledge Graphs +1

Memory-Based Semantic Parsing

no code implementations7 Sep 2021 Parag Jain, Mirella Lapata

We present a memory-based model for context-dependent semantic parsing.

Semantic Parsing

Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective

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.

Knowledge Graphs Translation

Unified Semantic Parsing with Weak Supervision

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.

Semantic Parsing

Unsupervised Neural Text Simplification

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.

Denoising Text Simplification

Scalable Micro-planned Generation of Discourse from Structured Data

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).

Knowledge Graphs Text Generation

Unsupervised Controllable Text Formalization

1 code implementation10 Sep 2018 Parag Jain, Abhijit Mishra, Amar Prakash Azad, Karthik Sankaranarayanan

We propose a novel framework for controllable natural language transformation.

Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization

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).

Story Generation from Sequence of Independent Short Descriptions

no code implementations18 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.

Machine Translation Story Generation +1

Topic Modeling Using Distributed Word Embeddings

no code implementations15 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.

Word Embeddings

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