Papers by Subendhu Rongali
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
Context Length Alone Hurts LLM Performance Despite Perfect Retrieval (2025.findings-emnlp)
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Yufeng Du, Minyang Tian, Srikanth Ronanki, Subendhu Rongali, Sravan Babu Bodapati, Aram Galstyan, Azton Wells, Roy Schwartz, Eliu A Huerta, Hao Peng
| Challenge: | Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. |
| Approach: | They propose a model-agnostic mitigation strategy that transforms a long-context task into a short-concept one by prompting the model to recite the retrieved evidence before attempting to solve the problem. |
| Outcome: | The proposed model improves on a long-context task up to 4% on RULER. |
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings (2020.findings-emnlp)
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Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao, Subendhu Rongali, Haidar Khan, Michael Kayser
| Challenge: | Existing task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders. |
| Approach: | They propose to learn compositional code embeddings to greatly reduce the sizes of BERT and RoBERTa encoders. |
| Outcome: | The proposed model reduces the size of BERT and RoBERTa encoders while maintaining performance. |
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders (2020.emnlp-main)
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| Challenge: | Syntactic parse trees are valuable intermediate features for many NLP pipelines. |
| Approach: | They propose an improved version of DIORA that encodes a single tree rather than a softly-weighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. |
| Outcome: | The proposed model improves state-of-the-art in constituency parsing on the English WSJ Penn Treebank by 2.2-6% F1, depending on the data used for fine-tuning. |
Improved Latent Tree Induction with Distant Supervision via Span Constraints (2021.emnlp-main)
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Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum
| Challenge: | Distant supervision is not a practical way to perform unsupervised syntactic parsing. |
| Approach: | They propose a technique that uses distant supervision to improve unsupervised constituency parsing by using phrase bracketing. |
| Outcome: | The proposed method improves constituency parsing on English WSJ Penn Treebank by more than 5 F1 compared with full parse tree annotations. |