Papers with open-domain scenarios
Towards Open-Domain Topic Classification (2022.naacl-demo)
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| Challenge: | Existing supervised classification models are insensitive to class names, but are no longer effective in open-domain tasks where the taxonomy is unbounded. |
| Approach: | They propose a topic classification system that accepts user-defined taxonomy in real time . they train a pretrained language model on a new Wikipedia dataset and train it on Wikipedia . |
| Outcome: | The proposed system improves over existing zero-shot models and performs competitively with weakly-supervised models trained on in-domain data. |
Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning (2025.emnlp-industry)
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Ruizhe Chen, Tianze Luo, Zhiting Fan, Heqing Zou, Zhaopeng Feng, Guiyang Xie, Hansheng Zhang, Zhuochen Wang, Zuozhu Liu, Zhang Huaijian
| Challenge: | Existing methods for video temporal grounding suffer from limited temporal awareness and poor generalization. |
| Approach: | They propose a two-stage training framework that integrates supervised fine-tuning with reinforcement learning to improve both the accuracy and robustness of VTG models. |
| Outcome: | The proposed training framework outperforms existing models on multiple benchmarks on open-domain and challenging scenarios. |
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension (P18-1)
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| Challenge: | Various advanced neural models have been proposed for reading comprehension, but most models ignore its relations with other answer candidates. |
| Approach: | They propose to model reading comprehension as an extract-then-select two-stage procedure . they first extract answer candidates from passages, then select the final answer by combining information from all candidates. |
| Outcome: | The proposed approach improves state-of-the-art performance on open-domain reading comprehension datasets. |
Don’t Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing rerankers are mainly trained on well-edited texts, but stylistic features can be misled by reranked models. |
| Approach: | They propose a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness. |
| Outcome: | Extensive experiments show that SARK improves generation performance across multiple LLMs under mixed-style conditions. |
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions, but their responses are often verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. |
| Approach: | They propose a strategy-enhanced role-playing framework that emulates real-world interactions and a dataset that is used to develop an emotional support agent. |
| Outcome: | The proposed framework emulates real-world interactions and promotes a broader range of dialogues and Emotional Support Agent training. |