Papers by Bishal Santra

Frugal Prompting for Dialog Models (2023.findings-emnlp)

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Bishal Santra, Sakya Basak, Abhinandan De, Manish Gupta, Pawan Goyal

Challenge:	Large language models (LLMs) are used in natural language processing tasks with an unrealistic speed and effectiveness.
Approach:	They propose more compact ways of providing dialog history information while ensuring good performance and reducing model’s inference-API costs.
Outcome:	The proposed models have the optimal usable-information density while maintaining good performance and reducing model’s inference-API costs.

Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages (P19-1)

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Amrith Krishna, Vishnu Sharma, Bishal Santra, Aishik Chakraborty, Pavankumar Satuluri, Pawan Goyal

Semantic Text Processing

Challenge:	Obtaining the proper word ordering, called as the prose ordering, from a verse is often considered a task which requires linguistic expertise.
Approach:	They propose a word ordering (linearisation) task that ignores the word arrangement at the verse side.
Outcome:	The proposed model outperforms current models in word ordering for the translation task in Sanskrit.

Chat-Ghosting: Methods for Auto-Completion in Dialog Systems (2026.eacl-long)

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Anubhab Mandal, Sandeep Mishra, Bishal Santra, Tushar Abhishek, Pawan Goyal, Manish Gupta

Dialogue Systems & Conversational Agents

Challenge:	Ghosting is a type-ahead completion task that predicts a user's intended input for inline query auto-completion (QAC).
Approach:	They propose to use ghosting to predict a user's intended input for inline query auto-completion by suggesting completions to incomplete queries.
Outcome:	The proposed method outperforms deep learning and deep learning methods with and without dialog context for ghosting.

Hierarchical Transformer for Task Oriented Dialog Systems (2021.naacl-main)

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Bishal Santra, Potnuru Anusha, Pawan Goyal

Natural Language Interfaces

Challenge:	Existing models for dialog generation are challenging to train using the standard Seq2Seq models.
Approach:	They propose a framework for Hierarchical Transformer Encoders that can be morphed into any hierarchical transformer by using specially designed attention masks and positional encodings.
Outcome:	The proposed framework can be morphed into any hierarchical encoder, including HRED and HIBERT like models, by using specially designed attention masks and positional encodings.

HORIZON: A Benchmark for In-the-wild User Behaviour Modeling (2026.findings-acl)

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Arnav Goel, Pranjal A Chitale, Bhawna Paliwal, Bishal Santra, Amit Sharma

Robustness in NLP

Challenge:	Existing user modeling benchmarks focus on short sessions and next-item prediction within a single domain.
Approach:	They propose a benchmark that reformulates user modeling along three axes . it covers 54M users and 35M items, enabling pretraining and evaluation . they propose tasks and evaluation setups that better reflect real-world deployment scenarios .
Outcome:	The proposed benchmark covers 54M users and 35M items, and is based on Amazon Reviews.

Representation Learning for Conversational Data using Discourse Mutual Information Maximization (2022.naacl-main)

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Bishal Santra, Sumegh Roychowdhury, Aishik Mandal, Vasu Gurram, Atharva Naik, Manish Gupta, Pawan Goyal

Dialogue Systems & Conversational Agents

Challenge:	Existing language modeling pretraining objectives do not take structural information of conversational text into account.
Approach:	They propose a structure-aware Mutual Information based loss-function DMI for training dialog-representation models that captures the inherent uncertainty in response prediction.
Outcome:	The proposed model outperforms strong baseline models on nine diverse tasks.

SCULPT: Systematic Tuning of Long Prompts (2025.acl-long)

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Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra, Parag Agrawal, Manish Gupta

Responsible & Trustworthy NLP

Challenge:	Existing methods for prompt optimization struggle with longer, more complex ones, often risking information loss and being sensitive to small perturbations.
Approach:	They propose a framework that treats prompt optimization as a hierarchical tree refinement problem and uses a Critic-Actor framework to generate reflections and apply actions to refine the prompt.
Outcome:	The proposed framework produces more stable and interpretable prompt modifications, ensuring better generalization across tasks.

Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit (D18-1)

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Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal

Syntactic Text Processing

Challenge:	a structured prediction framework is proposed to solve word segmentation and morphological tagging tasks in a free word order language.
Approach:	They propose a structured prediction framework that jointly solves word segmentation and morphological tagging tasks in Sanskrit.
Outcome:	The proposed model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data.

Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs (D19-1)

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Soumya Sharma, Bishal Santra, Abhik Jana, Santosh Tokala, Niloy Ganguly, Pawan Goyal

Semantic Text Processing

Challenge:	Recent studies have shown that structured domain knowledge can be used for textual inference tasks in the medical domain.
Approach:	They propose to integrate structured domain knowledge into a knowledge graph for the Medical NLI task.
Outcome:	The proposed approach improves the baseline BioELMo architecture for the Medical NLI task.

Router-Suggest: Dynamic Routing for Multimodal Auto-Completion in Visually-Grounded Dialogs (2026.eacl-industry)

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Sandeep Mishra, Devichand Budagam, Anubhab Mandal, Bishal Santra, Pawan Goyal, Manish Gupta

Multimodality

Challenge:	a task that grounds predictions in multimodal context is essential for chatbots, chatbot systems and healthcare consultations.
Approach:	They propose a task that grounds predictions in multimodal context to better capture user intent.
Outcome:	The proposed task can be used to predict upcoming characters in live chats using partially typed text and visual cues.

Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval (2025.emnlp-main)

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Pranjal A Chitale, Bishal Santra, Yashoteja Prabhu, Amit Sharma

Responsible & Trustworthy NLP

Challenge:	Existing research does not explore key factors such as optimal augmentation scale and the necessity of using large augmentation models.
Approach:	They propose to use LLMs to augment compact dual-encoder models to improve retrieval performance.
Outcome:	The proposed approach improves retrieval performance but its benefits diminish beyond a certain scale even with diverse augmentation strategies.