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• Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering
  Parag Jain, Mirella Lapata
  Under Review 2024
  TLDR We present a method to aggregate evidence from multiple sources into a dynamic graph for conversational question answering, efficiently integrating it with LLMs for end-to-end training. Past evidence is tracked in a memory module, which updates as the conversation evolves to influence the graph structure and representation.

• Structsum Generation for Faster Text Comprehension
  Parag Jain, Andreea Marzoca, Francesco Piccinno
  ACL 2024
  TLDR We explore generating structured representations (Mind Maps 🧠 🗺 and Tables 📝 ) of text using LLMs. Via a user study, we show that structured representations reduce comprehension time for users compared to just plain text.

• Conversational Semantic Parsing using Dynamic Context Graphs
  Parag Jain, Mirella Lapata
  EMNLP 2023 [Code]
  TLDR We model knowledge graph (KG) context as a dynamic context graph and implement a scalable method for context-dependent type linking. Our approach demonstrates improved performance on the SPICE dataset, which requires understanding long-range context.

• Semantic Parsing for Conversational Question Answering over Knowledge Graphs
  Laura Perez-Beltrachini, Parag Jain, Emilio Monti, Mirella Lapata
  EACL 2023 [Dataset and Code]
  TLDR We create SPICE, a semantic parsing dataset for conversational question answering over Wikidata.

• Multi-Document Summarization with Centroid-Based Pretraining
  Ratish Puduppully, Parag Jain, Nancy F. Chen, Mark Steedman
  ACL 2023 [Code]
  TLDR We introduce a novel pretraining objective for multi-document summarization, which involves selecting the ROUGE-based centroid of each document cluster as a proxy for its summary.

• Memory-Based Semantic Parsing
  Parag Jain, Mirella Lapata
  TACL 2021 -- [Video] [Code]
  TLDR We propose to represent discourse information using a bounded external memory. Bounded memory helps process long interactions irrespective of conversation-length. Our memory is interpretable and is managed by a learned controller.

• Unified Semantic Parsing with Weak Supervision
  Priyanka Agrawal, Parag Jain, Ayushi Dalmia, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
  ACL 2019 [Code]
  TLDR We propose a multi-policy distillation based method for learning a unified semantic parser (student) using independent weakly supervised experts (teacher) for each domain. Individual experts are trained using the REINFORCE to maximize the expected denotation accuracy.

• Scalable Micro-planned Generation of Discourse from Structured Data
  Anirban Laha*, Parag Jain*, Abhijit Mishra, Karthik Sankaranarayanan
  Computational Linguistics Journal, 2019 -- [Code]
  TLDR A modular, pipeline-based approach, for structured data to text. Our framework does not require task-specific parallel data.

  ✨In news: Powering Match Insights for 🎾 US Open by interfacing with structured knowledge bases. TOI Gadgetsnow IBM Article

• Unsupervised Controllable Text Formalization
  Parag Jain, Abhijit Mishra, Amar Prakash Azad, Karthik Sankaranarayanan
  AAAI 2019 [Video] -- [Code]
  TLDR Wake-sleep style algorithm for learning a controllable style transfer model in an unsupervised manner.

• Unsupervised Neural Text Simplification
  Sai Surya, Abhijit Mishra, Anirban Laha, Parag Jain, Karthik Sankaranarayanan
  ACL 2019 [Code]
  TLDR Adversarial loss based method for lexical and syntactic simplification without using parallel data.

• A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
  Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M Khapra, Shreyas Shetty
  NAACL-HLT 2018 [Code]
  TLDR Efficient hierarchical attention model for table-to-text with known table schema.

• Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
  Preksha Nema*, Shreyas Shetty*, Parag Jain*, Anirban Laha, Karthik Sankaranarayanan, Mitesh M Khapra
  NAACL-HLT 2018 [Video] -- [Code]
  TLDR Propose a method to induce stay-on and never-look-back behavior for structured data summarization.

• Metric Learning for Clustering in Streaming Large-Scale Data
  Parag Jain,
  IIT Hyderabad, 2015 Thesis
  TLDR Efficient methods for metric learning in streaming data scenarios. We extend diffusion maps for incremental data using incremental SVD. Additionally, we propose an unsupervised information-theoretic metric learning method based on laplacian eigenmaps.

• Talk at IIT Hyderabad on Structsum Generation for Faster Text Comprehension.

• Foster Talk at Jabalpur Engineering College, 2023

• Conversational Semantic Parsing - Online International. Conference. On. Advances in Physical, Mathematical and Computational Sciences, India - 2022

• Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective, Tutorial ACL 2019[Website]

• Introduction to Machine Learning - Christ University Bangalore, 2018

• Talk on Online Metric learning (IBM Research Bangalore, 2016) [Slides]

• Tutor for Natural Language Understanding, Generation, and Machine Translation, University of Edinburgh (2020-2021)

• TA for Numerical Linear Algebra for Data Analysis (CS5270, 2015), IIT Hyderabad

• TA for Introduction to Database Management Systems (CS3010/CS3011, 2014), IIT Hyderabad

• TA for Advanced Compiler Design (CS6240, 2014), IIT Hyderabad