| Challenge: | Recent models for zero pronoun resolution in Chinese are short-sighted and do not capture semantic information for zeros and candidate antecedents. |
| Approach: | They propose to integrate a deep reinforcement learning approach to Chinese zero pronoun resolution. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in three experimental settings. |
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Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT (2020.acl-main)
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| Challenge: | Zero pronoun recovery and resolution aim at recovering the dropped pronounce and pointing out its anaphoric mentions. |
| Approach: | They propose to solve two tasks together to recover the dropped pronoun and point out its anaphoric mentions. |
| Outcome: | The proposed model outperforms previous state of the arts benchmarks on two benchmarks. |
Knowledge-aware Pronoun Coreference Resolution (P19-1)
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| Challenge: | Existing models for pronoun coreference resolution only use triplets, the most common format for knowledge graphs. |
| Approach: | They propose a model that leverages different types of knowledge to resolve pronoun coreference with a neural model. |
| Outcome: | The proposed model outperforms state-of-the-art baselines on two datasets from different domains. |
Zero Pronouns Identification based on Span prediction (2021.acl-srw)
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| Challenge: | Pro-drop languages allow omissions of essential phrases or arguments . the presence of zero-pronouns affects downstream tasks of NLP . |
| Approach: | They propose a query-based method to identify zero-pronoun arguments . they use Japanese and Chinese datasets to evaluate the method . |
| Outcome: | The proposed method surpasses the sequence labeling baseline on Japanese and Chinese datasets. |
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (D19-1)
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| Challenge: | Large-scale training sets for pronoun resolution are scarce, since manually labelling data is costly. |
| Approach: | They propose a language-model-based approach to solve pronoun disambiguation problems using a WikiCREM dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on 6 out of 7 datasets. |
Deep Reinforcement Learning for Entity Alignment (2022.findings-acl)
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| Challenge: | Entity alignment (EA) methods identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. |
| Approach: | They propose to model entity alignment as a sequential decision-making task where an agent sequentially decides whether two entities are matched or mismatched based on representation vectors. |
| Outcome: | The proposed framework consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1. |
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (D18-1)
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| Challenge: | Pronouns are often omitted in pro-drop languages, such as Chinese . this leads to various translation problems in terms of completeness, syntax and semantics . |
| Approach: | They propose a reconstruction-based approach to alleviate dropped pronoun (DP) translation problems for neural machine translation models by employing a shared reconstructor and a joint learning approach. |
| Outcome: | The proposed approach improves translation performance and accuracy of DP predictions. |
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)
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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng
| Challenge: | Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment. |
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| Outcome: | The proposed training recipe bridges atomic action execution and strategic task completion. |
Incorporating Context and External Knowledge for Pronoun Coreference Resolution (N19-1)
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| Challenge: | Existing models for pronoun coreference resolution rely on manual definitions and features to resolve pronounous coreferences. |
| Approach: | They propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models by a large margin. |
Z-coref: Thai Coreference and Zero Pronoun Resolution (2024.acl-srw)
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| Challenge: | limited research and datasets pose significant challenges in Thai language . a proposed model capable of simultaneously handling CR and ZPR tasks takes less time to train . |
| Approach: | They propose to annotate a Thai-based CR and ZPR dataset and introduce a model that can handle both tasks by adjusting the span definition to include token gaps. |
| Outcome: | The proposed model outperforms the state-of-the-art in resolving both coreference resolution and zero-pronoun resolution while taking less time to train. |