Papers by Rong Pan
The TechQA Dataset (2020.acl-main)
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Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang
| Challenge: | TECHQA is a domain-adaptation question answering dataset for the technical support domain. |
| Approach: | They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum . |
| Outcome: | The TECHQA dataset highlights two real-world issues from the automated customer support domain. |
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data (2024.emnlp-main)
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| Challenge: | Existing calibration methods negatively impact model accuracy due to the lack of diversity of validation data. |
| Approach: | They propose a calibration method that incorporates synthetic data without compromising accuracy. |
| Outcome: | The proposed method improves model accuracy on real data and reduces calibration error by 34% on four different tasks. |
FocusLLM: Precise Understanding of Long Context by Dynamic Condensing (2025.acl-long)
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Zhenyu Li, Yike Zhang, Tengyu Pan, Yutao Sun, Zhichao Duan, Junjie Fang, Rong Han, Zixuan Wang, Jianyong Wang
| Challenge: | Existing context condensing methods cannot accurately understand the full context, as there is a considerable amount of information loss in the condensed process. |
| Approach: | They propose a framework to extend the fixed context length of any decoder-only LLM by distilling crucial information from long sequences. |
| Outcome: | The proposed framework extends the fixed context length of any decoder-only LLM, allowing it to focus on relevant information from very long sequences. |
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)
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| Challenge: | Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data. |
| Approach: | They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data. |
| Outcome: | The proposed framework improves the fidelity of the generated texts to the input structured data. |
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)
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| Challenge: | Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining. |
| Approach: | They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components. |
| Outcome: | The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models. |
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
| Outcome: | Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator. |