Papers with learning-based methods
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications (D19-53)
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| Challenge: | Existing methods for relation extraction rely on lexical patterns and dependency templates. |
| Approach: | They propose a rule-based method for extracting entity networks from scientific literature . they use syntactic features of constituent parsing trees to extract and construct graphs . |
| Outcome: | The proposed method outperforms state-of-the-art methods in several cases. |
OTExtSum: Extractive Text Summarisation with Optimal Transport (2022.findings-naacl)
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| Challenge: | Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. |
| Approach: | They propose to formulate extractive text summarisation as an Optimal Transport (OT) problem and use it to obtain an optimal summary that minimises the transportation cost to a given document. |
| Outcome: | The proposed method outperforms state-of-the-art methods and learning-based methods on multiNews, PubMed, BillSum, and CNN/DM datasets. |
Grammar-Based Patches Generation for Automated Program Repair (2021.findings-acl)
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| Challenge: | Automated program repair (APR) aims to find an automatic solution to program language bugs without human intervention. |
| Approach: | They propose a grammar-based rule-rule model which regards the repair process as the transformation of grammar rules and employs a tree-based self-attention approach to guarantee grammar correctness. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a Java dataset in terms of generated code accuracy. |
AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)
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Sun Hui, Ding Yanfeng, Huidong Ma, Chang Xu, Keyan Jin, Lizheng Zu, Cheng Zhong, Xiaoguang Liu, Gang Wang, Wentong Cai
| Challenge: | Lossless compression has made significant advancements in Genomics Data storage, sharing and management. |
| Approach: | They propose a novel agent-based GD Compressor with 3 layers with a multi-agent named Leader and Worker. |
| Outcome: | The proposed method improves on existing methods with low-level modeling and limited adaptability and user-unfriendly interface. |
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)
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Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. |
| Approach: | They propose a framework that allows large language models to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
| Outcome: | The proposed framework enables LLMs to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation (2024.acl-long)
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| Challenge: | Controllable text generation (CTG) focuses on crafting texts adhering to specific attributes . studies show learning-based methods require extensive computational and data resources . |
| Approach: | They propose a learning-free approach that dynamically adjusts the weights of selected feedforward neural network vectors to steer the outputs of large language models. |
| Outcome: | The proposed approach outperforms learning-based and learning-free methods on multi-attribute control. |
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. |
Accelerating Transformer Inference for Translation via Parallel Decoding (2023.acl-long)
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Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodola
| Challenge: | Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT) Existing methods to solve this problem are expensive and require changes to the model. |
| Approach: | They propose to reframe autoregressive decoding with a parallel formulation . they propose to speed up existing models without training or modifications while retaining translation quality. |
| Outcome: | The proposed model speeds up existing models without training or modifications while retaining translation quality. |