Papers by Shashank Jain
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
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Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
Mind the Query: A Benchmark Dataset towards Text2Cypher Task (2025.emnlp-industry)
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| Challenge: | Graph databases store data in nodes and relationships, enabling more natural modeling of complex, interconnected data. |
| Approach: | They present a high-quality dataset for the Text2Cypher task . it is enabling the translation of natural language (NL) questions into executable Cypher queries over graph databases. |
| Outcome: | The proposed dataset includes 27,529 NL queries and corresponding Cyphers spanning across 11 real-world graph datasets. |
Does Social Pressure Drive Persuasion in Online Fora? (2021.emnlp-main)
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| Challenge: | Using social features, we hypothesize that comments from the ambient community can either affirm the original view or implicitly exert pressure to change it. |
| Approach: | They propose a structured model to capture the ambient community’s sentiment towards the discussion and its effect on persuasion. |
| Outcome: | The proposed model captures the ambient community’s sentiment towards the discussion and its effect on persuasion. |
Explaining Differences Between Model Pairs in Natural Language through Sample Learning (2025.emnlp-main)
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| Challenge: | a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions requires access to training data. |
| Approach: | They propose a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions. |
| Outcome: | The proposed framework generates faithful natural language explanations of when and how two models diverge in their predictions. |
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model (2024.emnlp-industry)
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Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar
| Challenge: | Prior work on LLMs focused on models that combine text and one other modality, such as image encoders or proprietary models that are not open sourced. |
| Approach: | They propose a unified model that reasons over diverse input modality signals and generates textual responses. |
| Outcome: | The proposed model performs better on multimodal tasks than industry-leading models . |
A Study on the Efficiency and Generalization of Light Hybrid Retrievers (2023.acl-short)
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Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
| Challenge: | Recent research focuses on building neural retrievers which learn dense embeddings of query and document into a semantic space. |
| Approach: | They propose to use an indexing-efficient dense retriever to reduce hybrid retrievers' memory by using the state-based indexing algorithm. |
| Outcome: | The proposed hybrid retriever saves 13 memory while maintaining 98.0% performance on out-of-domain datasets and adversarial attacks datasets. |
Building Adaptive Acceptability Classifiers for Neural NLG (2021.emnlp-main)
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Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White
| Challenge: | Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models. |
| Approach: | They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models. |
| Outcome: | The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training. |