## 1 Introduction  
Aspect-Based Sentiment Analysis (ABSA) is an essential field in natural language processing. The ABSA task [1, 2] aims to extract Aspect Terms (AT) and Opinion Terms (OT) from a given sentence and analyze the Sentiment Polarity (SP) and Aspect Category (AC) of aspect terms. For example, in the sentence \"The computer is beautiful, but the keyboard is uncomfortable.\", \"computer\" and \"keyboard\" are aspect terms, while \"beautiful\" and \"uncomfortable\" are opinion terms, thus indicating that the sentiment polarities of \"computer\" and \"keyboard\" are \"positive\" and \"negative\", respectively. The Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction task proposed by [3] includes implicit sentiment, allowing for a more precise expression of sentiment. As shown in Figure 1, in restaurant reviews, the sentence \"I've waited over one hour for food\" does not contain aspect terms or opinion terms but expresses negative sentiment towards the restaurant's service. The quadruple (NULL, service general, NULL, NEG) can be extracted from the sentence. For the ACOS task, [PERSON] et al. [3] firstly established a series of pipeline baselines to extract quadruples by modeling them step by step through two extraction tasks and two classification tasks. However, this multi-stage propagation approach can lead to severe error propagation. More research has transformed the quadruple prediction task into a text generation task, adopting an end-to-end approach to predict quadruples [4, 5, 6]. [PERSON] et al. [2] transformed the quadruple extraction task into a paraphrasing generation problem, where the attributes of elements {(AT, AO, OT, SP)} correspond to the elements\(\{\mathcal{X}_{\text{AT}},\mathcal{X}_{\text{AC}},\mathcal{X}_{\text{SP}}, \mathcal{X}_{\text{OT}}\}\) in a fixed element order filled into templates. This method has become a new paradigm. Generation-based aspect-level sentiment analysis models inherently impose a sequence on the quadruplet elements despite their inherent unordered nature. [PERSON] et al. [7] experimentally verified that different ordering of elements had different effects on the results. [PERSON] et al. [8] advanced an element order-based prompt learning method considering multiple element orders to build multiple views. However, their methodology employs pretrained models to select an optimal order for input, obtaining a single prediction using different strategies without engaging in interactive template manipulation during the model's training and inference processes. Inspired by [7; 8], we propose a dual-template prompted mutual learning generative model to solve the ACOS task. Firstly, according to the disorder of quadruple elements, we set two prompt templates, and the elements in each template are marked by specific labels. Then, we construct a T5-based generation framework to generate quadruples. Secondly, to enhance the model's ability to capture implicit words, we set the enhanced interaction module of marker-Tag, a label classification-enhanced decoding module, in the decoding stage. We add auxiliary suffixes to the input sequence to make the implicit words explicit and introduce the BIO tag, which combines the specific marker features in the template to share the explicit and implicit aspect words and opinion words in the sentence in the decoding stage of the model. In addition, we set up a mutual learning enhancement module to interact with the label information of the two template generation process decoders, make full use of the marker dependence to break the sequential decoding limit of the decoder, and learn the marker information iteratively and interactively. The main contributions of this paper are as follows:  
1. In the ACOS task, we propose a dual-template prompted quadruple generation model based on the disorder of quadruple elements. Based on marker template features, we develop mutual learning enhancement modules in the decoding process of the dual template generation to interact with the iterative label information. 2.