Papers with IS
Does BERT Know that the IS-A Relation Is Transitive? (2022.acl-short)
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| Challenge: | Recent studies suggest pre-trained BERT can capture lexico-semantic clues from words in context. |
| Approach: | They examine word senses and the transitive property of IS-A relation . they aim to quantify how much BERT agrees with transitivity property . |
| Outcome: | The proposed model can capture lexico-semantic clues from words in context . but to what extent it captures transitive nature of some lexical relations is unclear . |
A Strong and Robust Baseline for Text-Image Matching (P19-2)
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| Challenge: | Text-image matching is one of the most popular methods for training text-image embeddings. |
| Approach: | They propose to use a kNN-margin loss that utilizes hard negatives and is robust to noise . they advocate using Inverted Softmax and Cross-modal Local Scaling during inference . |
| Outcome: | The proposed loss function is robust to noise and pseudo negatives are tolerable . the proposed loss functions improve scores of all metrics by a large margin . |
End-to-end Neural Information Status Classification (2021.findings-emnlp)
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| Challenge: | Existing studies on information status classification and bridging anaphora recognition assume that gold mention or syntactic tree information is given. |
| Approach: | They propose an end-to-end neural approach for information status classification using a mention extraction component and an information status assignment component. |
| Outcome: | The proposed system achieves state-of-the-art on fine-grained IS classification based on gold mentions and better than baselines on ISNotes and SciCorp. |
Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning (2025.findings-emnlp)
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| Challenge: | Existing methods for in-context learning (ICL) performance rely on quality and ordering of demonstrations. |
| Approach: | They propose a method that models iterative demonstration selection as a Markov Decision Process and craft hybrid reward signals. |
| Outcome: | The proposed method combines outcome-based accuracy signals with process-oriented signals like stepwise influence and label entropy improvement. |
Fine-grained Information Status Classification Using Discourse Context-Aware BERT (2020.coling-main)
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| Challenge: | Existing work on fine-grained information status (IS) relies on many hand-crafted linguistic features. |
| Approach: | They propose a discourse context-aware BERT model for fine-grained IS classification . they show an improvement of 10.5 F1 points for bridging anaphora recognition . |
| Outcome: | The proposed model achieves 4.8 absolute accuracy improvement on ISNotes corpus compared to previous work on bridging anaphora recognition . |
Label Embedding using Hierarchical Structure of Labels for Twitter Classification (D19-1)
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| Challenge: | Twitter is used for disaster monitoring and news material gathering . we propose a method that can consider the hierarchical structure of labels and labels themselves . |
| Approach: | They propose a method that can consider the hierarchical structure of labels and label texts themselves. |
| Outcome: | The proposed method outperforms the methods of the conference participants over the text REtrieval Conference (TREC) 2018 Incident Streams (IS) dataset. |
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking (2026.acl-long)
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Zechen Sun, Yuyang Sun, Zecheng Tang, Juntao Li, Wenpeng Hu, Wenliang Chen, Zhunchen Luo, Guotong Geng, Min Zhang
| Challenge: | Existing models with reasoning capabilities suffer from a severe length collapse in open-ended writing . |
| Approach: | They propose a framework that embeds a dynamic plan-write-reflect cycle into the generation process and train a model with interleaved reasoning traces. |
| Outcome: | The proposed framework achieves state-of-the-art performance on long-form benchmarks compared to other models on the same dataset. |
Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)
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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao
| Challenge: | Existing information-seeking (IS) agents rely on the web for their information acquisition. |
| Approach: | They propose a browser-action framework that decouples interaction control from page exploration through a nested structure. |
| Outcome: | Empirical results show that NestBrowse offers clear benefits in practice. |
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation (2026.findings-acl)
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| Challenge: | Existing self-evaluation methods rely on a model’s ability to estimate the correctness of its own outputs, but they depend heavily on language priors and are therefore ill-suited for evaluating vision-conditioned predictions. |
| Approach: | They propose a vision-aware uncertainty quantification framework that measures how strongly a model’s output depends on visual evidence. |
| Outcome: | The proposed framework outperforms existing methods across multiple datasets. |
LLM-based Rumor Detection via Influence Guided Sample Selection and Game-based Perspective Analysis (2025.acl-long)
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Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li
| Challenge: | Existing methods for rumor detection on social media are limited by limited modeling capacity and insufficient training corpora. |
| Approach: | They propose an SFT-based rumor detection model with Influence guided Sample selection and Game-based multi-perspective analysis to address these issues. |
| Outcome: | The proposed model outperforms existing SOTA on three datasets. |