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.

提出了一种在 Aspect Sentiment Triplet Extraction 中利用预训练表示的方法，并通过整合简化标签方案和新颖的令牌级对比学习策略来改善和利用预训练表示，实现了与最先进技术相媲美或更好的性能，同时具有更紧凑的设计和较低的计算开销。此外，我们还首次对 GPT-4 在少样本学习和连续思考场景下进行了评估，结果表明即使在大语言模型时代，预训练微调范式仍然非常有效。

MiniConGTS: 一个极简的对比网格标签方案，用于方面情感三元组抽取

MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme  for Aspect Sentiment Triplet Extraction

Sequence tagging models for constituent parsing are faster, but less accurate
than other types of parsers. In this work, we address the following weaknesses
of such constituent parsers: (a) high error rates around closing brackets of
long constituents, (b) large label sets, leading to sparsity, and (c) error
propagation arising from greedy decoding. To effectively close brackets, we
train a model that learns to switch between tagging schemes. To reduce
sparsity, we decompose the label set and use multi-task learning to jointly
learn to predict sublabels. Finally, we mitigate issues from greedy decoding
through auxiliary losses and sentence-level fine-tuning with policy gradient.
Combining these techniques, we clearly surpass the performance of sequence
tagging constituent parsers on the English and Chinese Penn Treebanks, and
reduce their parsing time even further. On the SPMRL datasets, we observe even
greater improvements across the board, including a new state of the art on
Basque, Hebrew, Polish and Swedish.

本文提出一种有效的方法来使 constituent parsing 更加准确，通过学习切换标记方案、降低标签集的稀疏性、并细化多任务学习以减少错误传播，加上辅助损失和策略梯度句子级微调等技术，成功优化了英文和中文 Penn Treebanks 数据集的连续标记 constituent parsers 的性能，并在 SPMRL 数据集上实现了更大的性能提升，包括在巴斯克语、希伯来语、波兰语和瑞典语上建立了新的 state of the art。