Papers by Soumya 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. |
Entity Exchange in the Wild: A Diagnostic Study of LLM Based Real-World Conversational Entity Extraction (2026.acl-industry)
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| Challenge: | Prior work has examined the impact of transcription noise and cross-turn reasoning, but it has not systematically analyzed how entity-exchange phenomena themselves shape extraction performance. |
| Approach: | They evaluate 16 large language models on 6,387 real-world customer–agent conversations spanning 12 entity types across numeric, alphanumeric, temporal, and free-text categories. |
| Outcome: | The proposed model improves on the extracted entities across all three axes yielding average gains of up to 6.4% across models. |
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. |