MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction
<|reference_start|>MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction: Aspect Sentiment Triplet Extraction (ASTE) aims to co-extract the sentiment triplets in a given corpus. Existing approaches within the pretraining-finetuning paradigm tend to either meticulously craft complex tagging schemes and classification heads, or incorporate external semantic augmentation to enhance performance. In this study, we, for the first time, re-evaluate the redundancy in tagging schemes and the internal enhancement in pretrained representations. We propose a method to improve and utilize pretrained representations by integrating a minimalist tagging scheme and a novel token-level contrastive learning strategy. The proposed approach demonstrates comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead. Additionally, we are the first to formally evaluate GPT-4's performance in few-shot learning and Chain-of-Thought scenarios for this task. The results demonstrate that the pretraining-finetuning paradigm remains highly effective even in the era of large language models.<|reference_end|>
@article{sun2024minicongts:,
    title={MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme
  for Aspect Sentiment Triplet Extraction},
    author={Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu},
    journal={arXiv preprint arXiv:2406.11234},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11234},
    primaryClass={cs.CL cs.AI}
}