Papers by Lucian Popa
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations (2023.acl-long)
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| Challenge: | Human-annotated labels and explanations are critical for training explainable NLP models. |
| Approach: | They propose a metric that measures the usefulness of an explanation for model performance at both fine-tuning and inference. |
| Outcome: | The proposed metric can evaluate the quality of human-annotated explanations, while Simulatability falls short. |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming (2022.findings-acl)
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Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
| Challenge: | supervised machine learning requires large amounts of labeled data to train models. |
| Approach: | They propose a framework to generate human-interpretable labeling functions . they propose to learn a model on the same labeled dataset and unlabeled data . |
| Outcome: | The proposed framework outperforms prior approaches on several text classification datasets. |
Learning Structured Representations of Entity Names using Active Learning and Weak Supervision (2020.emnlp-main)
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| Challenge: | Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. |
| Approach: | They propose a framework that combines active learning and weak supervision to solve this problem. |
| Outcome: | The proposed framework enables learning of high-quality models from a dozen labeled examples. |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation (2024.findings-emnlp)
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| Challenge: | Existing iterative retrieval-augmented generation approaches struggle to delve deeply into each facet of complex queries. |
| Approach: | They propose a framework that employs a tree-structured retrieval approach to enhance the depth and relevance of retrieved content. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multiple datasets and a newly introduced dataset. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
| Approach: | They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches. |
| Outcome: | The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods. |
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations (2026.findings-acl)
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| Challenge: | Several benchmarks have been released to evaluate model performance on multi-turn retrieval augment generation tasks. |
| Approach: | They propose to benchmark 666 conversations with over 2,800 conversation turns across 6 domains and a corpora that focuses on unanswerable questions and later conversation turns. |
| Outcome: | The proposed benchmarks show that retrieval and generation models struggle on conversations with UNanswerable, UNderspecified, and NONstandalone questions and UNclear responses. |
Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)
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| Challenge: | Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics. |
| Approach: | They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques . |
| Outcome: | The proposed method demonstrates robustness across multiple model architectures and noise levels. |
Low-resource Deep Entity Resolution with Transfer and Active Learning (P19-1)
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| Challenge: | Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. |
| Approach: | They propose a deep learning-based method that targets low-resource settings for ER by combining transfer learning and active learning. |
| Outcome: | The proposed method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels. |
Domain Representative Keywords Selection: A Probabilistic Approach (2022.findings-acl)
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| Challenge: | a probabilistic approach to select a subset of a target domain representative keywords is crucial for many downstream tasks in natural language processing. |
| Approach: | They propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain. |
| Outcome: | The proposed approach provides more importance to distinctive keywords than common keywords contrasting with the context domain. |