Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction (2025)

Abstract: Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opinions independently, optionally adding pairwise relations, often leading to error propagation and high time complexity. To address these challenges and being inspired by transition-based dependency parsing, we propose the first transition-based model for AOPE and ASTE that performs aspect and opinion extraction jointly, which also better captures position-aware aspect-opinion relations and mitigates entity-level bias. By integrating contrastive-augmented optimization, our model delivers more accurate action predictions and jointly optimizes separate subtasks in linear time. Extensive experiments on four commonly used ASTE/AOPE datasets show that, our proposed transition-based model outperform previous models on two out of the four datasets when trained on a single dataset. When multiple training sets are used, our proposed method achieves new state-of-the-art results on all datasets. We show that this is partly due to our model’s ability to benefit from transition actions learned from multiple datasets and domains.Our code is available at https://github.com/Paparare/trans_aste.

## introduction aspect-based sentiment analysis (absa) is a fine-grained sentiment analysis task that identifies specific aspects in text and analyzes the sentiments linked to them @xcite @xcite . as shown in figure 1 , absa involves subtasks such as aspect extraction (ae) and opinion extraction (oe)-identifying mentioned aspects and their related opinions, or the combination-aspect-opinion pair extraction. once the aspect and opinion have been extracted, figure 1 : demonstration of the processing steps in both classic and transitional methods for extracting aspectopinion pairs. importantly, our proposed transitional method predicts transition actions, and performs pair extraction after the aspect-opinion relationship has been established, allowing the model to capture contextual relationships more effectively. a sentiment is usually computed, and this more complicated task is often referred to as aspect-sentiment triplet extraction (aste). for instance, given the sentence: "gourmet food is delicious. good service, but not so welcoming", ae identifies gourmet food and service as aspects, while oe extracts delicious, good, and not so welcoming as opinions. these outputs are then combined to form aspect-opinion pairs, with a separate sentiment tagging system assigning polarities to create triplets @xcite @xcite . aste is the most integrated task for aspectbased sentiment analysis, for which diverse models leveraging various methodologies have been developed, including pipeline-based approach @xcite , sequence-to-sequence method @xcite , sequence-tagging method @xcite , to name just a few. despite these efforts and growing interests, the accuracy of recent models remains suboptimal, with the best systems scoring 60% or 70% @xcite . there are two key challenges that hinder performance: (1) disconnected aspect-opinion extraction: opinions are often extracted independently from their corresponding aspects @xcite . while positional relationships can be added as an auxiliary factor to assist pair extraction @xcite , this approach loses critical contextual information by treating aspects and opinions as separate entities. this limits the effectiveness of many token-based extraction methods. (2) high time complexity with longer sequences: methods using 2d matrix tagging @xcite to capture relationships between tokens face significant increases in time complexity as the length of the token sequence increases. this computational burden restricts their scalability, especially for longer texts in practical applications. to address these two challenges, we present the first transition-based aope system named trans-aope that (1) extracts the aspect and the opinion at the same time, and (2) has a time complexity of o(n). we also introduce a contrastiveaugmented optimization method to enhance model efficiency. we conduct experiments on 4 commonly used abas datasets, and compare our system with previous models. our results show that trans-model achieves state-of-the-art performance on all datasets we tested. we conduct comprehensive ablation studies to evaluate the contribution of optimization components and perform extensive training on various datasets to identify precisely where our model and baselines derive their learning. our contributions are: (1) we propose the first transition-based model that extracts aspect-opinion pairs based on relational aspects, rather than using relational factors as supplementary references or confirmation, with linear time complexity. (2) we experiment with a contrastive-augmented optimization method and find that balanced weighting yields faster, more stable improvements, emerging as the optimal training configuration. (3) we explore various training strategies and show that our proposed method achieves optimal performance on four datasets when trained on combined training sets, with better cross-dataset generalization. transition-based methods in nlp transitionbased approaches are widely used in dependency parsing, leveraging shift-reduce and bidirectional arc actions (left-arc, right-arc) for efficient o(n) parsing (aho and @xcite @xcite . these parsers maintain stack, buffer, and arc relations to track transitions and then build up dependency relations between tokens. transition-based methods have also been applied to various nlp tasks, including token segmentation @xcite , argument mining @xcite , constituency parsing @xcite , amr parsing @xcite , and sequence labeling (gómez-rodríguez et al., 2020), among others. transition-based methods have been explored in emotion analysis @xcite . in sentiment analysis, however, transition-based models have not been widely adopted. one exception is their use in generating graph structures for opinion extraction @xcite , although this design relies on graph embeddings and thus results in a time complexity of o(n 2 ), with performance that lags behind more recent aope and aste approaches. ## the trans-aope/aste model we recast aspect-opinion extraction as a parsingguided graph-construction problem in two stages: trans-aope incrementally extracts aspect-opinion pairs from context-rich inputs, and trans-aste tags the recovered pairs. the parser tracks five working structures-stack, buffer, aspect set, opinion set, and pair set-extending the three used in earlier systems and enabling joint recovery of aspects and opinions. ## results and analysis we study wall-clock training time per batch and peak gpu memory per batch because these two metrics jointly determine scalability (how fast larger datasets can be processed) and deployability (whether the model fits on commodity gpus). as shown in figure 3 , trans-aste has the smallest footprint on both counts. then, we present results obtained under various training-data configurations and compare our model with previous methods (section 5.1). finally, we assess the impact of adding a contrastive-loss term and identify the optimal loss configuration (section 5.2), and section 5.3 provides a detailed discussion of these findings. ## conclusion and future work in this paper, we present an efficient transition pipeline for the extraction of aspects-opinion pairs with linear time complexity o(n), enhanced by a contrastive-based optimization method. this approach obviates the need to directly identify and extract individual tokens, thereby mitigating tokenlevel bias. it can be trained on a combination of diverse datasets that offers the most comprehensive coverage of the actions needed, resulting in significant performance improvements across various datasets. specifically, training our model on a well-covered fused dataset enables it to learn robust action patterns, leading to superior performance on all datasets. our model surpasses retrained baseline models on the same fused dataset, establishing new state-of-the-art results for both aope and aste tasks. as transition-based methods have remained relatively less explored in sentiment-related tasks, we believe our work shows a promising direction to employ such methods in aspect-based sentiment analysis. future work can further examine the potential of transition-based models in other sentiment analysis tasks, as well as the generalization ability of these models in situations of multidomain data. it is also important to better understand the cross-domain and multi-domain generalization ability of transition models, by experimentation on more domains, since only two domains are involved in this work.