Papers with SOTA method
Video-guided Machine Translation with Spatial Hierarchical Attention Network (2021.acl-srw)
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| Challenge: | Existing studies use pretrained motion detection models as verb sense ambiguity representations to solve the verb sense problem. |
| Approach: | They propose to use video contents as auxiliary information to address the word sense ambiguity problem in machine translation. |
| Outcome: | Experiments on the VATEX dataset show that the proposed system achieves 35.86 BLEU-4 score, which is 0.51 score higher than the single model of the SOTA method. |
LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)
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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Structured knowledge grounding (SKG) tasks are a key part of many NLP applications. |
| Approach: | They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format . |
| Outcome: | The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning. |
SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER (2022.coling-1)
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| Challenge: | Existing methods to solve Unlabeled Entity Problem (UEP) in Named Entities Recognition datasets are not effective in real-world datasets. |
| Approach: | They propose to decrease the distance of span representations with the same label while increasing it for different ones via span-based contrastive learning. |
| Outcome: | The proposed method outperforms the previous method on two real-world datasets. |
Parameter-free Automatically Prompting: A Latent Pseudo Label Mapping Model for Prompt-based Learning (2022.findings-emnlp)
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| Challenge: | Existing manual label mapping methods that require extra parameters and human knowledge are limited in data. |
| Approach: | They propose a Latent Pseudo Label Mapping method that optimizes the label mapping without human knowledge and extra parameters. |
| Outcome: | The proposed method outperforms the standard SOTA method in few-shot learning tasks and significantly outperformed the standard ALM method which requires extra task-specific prior knowledge. |
Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions . |
| Approach: | They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions . |
| Outcome: | The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas. |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
| Approach: | They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently . |
| Outcome: | The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width. |
Rethinking and Improving Multi-task Learning for End-to-end Speech Translation (2023.emnlp-main)
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| Challenge: | auxiliary tasks are highly consistent with end-to-end speech translation (ST) but their effectiveness has not been thoroughly studied. |
| Approach: | They propose an improved multi-task learning approach for the ST task that bridges the modal gap by mitigating the difference in length and representation. |
| Outcome: | The proposed approach achieves state-of-the-art on the MuST-C dataset with 20.8% of training time required by the current SOTA method. |
UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)
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| Challenge: | Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages. |
| Approach: | They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence. |
| Outcome: | The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments. |