Adarsh Pyarelal

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Scholarly Contributions

Journals/Publications

• Erikson, J. A., Alt, M., Pyarelal, A., & Kapa, L. (2024). Science Vocabulary and Science Achievement in Children with Developmental Language Disorder and typical Language Development. Language, Speech, and Hearing Services in Schools.
• Kling, F., Pyarelal, A., & Su, S. (2015). Light Charged Higgs Bosons to AW/HW via Top Decay. Journal of High Energy Physics, 11, "051".

Proceedings Publications

• Noriega-Atala, E., Vacareanu, R., Ashton, S. T., Pyarelal, A., Morrison, C. T., & Surdeanu, M. (2024). When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context. In EMNLP 2024 Findings.
• Soares$^circ$, P., Pyarelal, A., Krishnaswamy, M., Butler, E., & Barnard, K. (2024). Probabilistic Modeling of Interpersonal Coordination Processes. In Forty-first International Conference on Machine Learning (ICML 2024).
• Surdeanu, M., Morrison, C. T., Pyarelal, A., Torres Ashton, S., Vacareanu, R., & Noriega-Atala, E. (2024). When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context. In Findings of the Association for Computational Linguistics: EMNLP 2024.
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  Abstract: We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event.
• Zhang, L., Lieffers, J., Shivanna, P., & Pyarelal, A. (2024). Deep Reinforcement Learning with Vector Quantized Encoding. In RLC Workshop on Interpretable Policies in Reinforcement Learning (InterpPol) 2024.
• "Miah, M., Pyarelal, A., & Huang, R. (2023, dec). Hierarchical Fusion for Online Multimodal Dialog Act Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023.
• "Qamar, A., Pyarelal, A., & Huang, R. (2023, dec). Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023.
• Pyarelal, A., Duong, E., Shibu, C. J., Soares, P., Boyd, S., Khosla, P., Pfeifer, V., Zhang, D., Andrews, E. S., Champlin, R., Raymond, V. P., Krishnaswamy, M., Morrison, C., Butler, E., & Barnard, K. (2023). The ToMCAT Dataset. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
• Surdeanu, M., Morrison, C. T., Barnard, J. J., Bethard, S. J., Paul, M., Luo, F., Lent, H., Tang, Z., Bachman, J. A., Yadav, V., Nagesh, A., Valenzuela-Escárcega, M. A., Laparra, E., Alcock, K., Gyori, B. M., Pyarelal, A., & Sharp, R. (2019). Eidos & Delphi: From Free Text to Executable Causal Models. In Modeling the World’s Systems, 2019.