Aspect-Based Sentiment Analysis (ABSA) in tourism plays a significant role in understanding tourists’ evaluations of specific aspects of attractions, which is crucial for driving innovation and development in the tourism industry. However, traditional pipeline models are afflicted by issues, such as error propagation and incomplete extraction of sentiment elements. To alleviate this issue, this paper proposes an aspect-based sentiment analysis model, ACOS_LLM, for Aspect-Category-Opinion-Sentiment Quadruple Extraction (ACOSQE). The model comprises two key stages: auxiliary knowledge generation and ACOSQE. Firstly, Adalora is used to fine-tune large language models for generating high-quality auxiliary knowledge. To enhance model efficiency, Sparsegpt is utilized to compress the fine-tuned model to 50% sparsity. Subsequently, Positional information and sequence modeling are employed to achieve the ACOSQE task, with auxiliary knowledge and the original text as inputs. Experiments are conducted on both self-created tourism datasets and publicly available datasets, Rest15 and Rest16. Results demonstrate the model’s superior performance, with an F1 improvement of 7.49% compared to other models on the tourism dataset. Additionally, there is an F1 improvement of 0.05% and 1.06% on the Rest15 and Rest16 datasets, respectively.

Recent research has shown a burgeoning interest in exploring sparse models for massively Multilingual Neural Machine Translation (MNMT). In this paper, we present a comprehensive survey of this emerging topic. Massively MNMT, when based on sparse models, offers significant improvements in parameter efficiency and reduces interference compared to its dense model counterparts. Various methods have been proposed to leverage sparse models for enhancing translation quality. However, the lack of a thorough survey has hindered the identification and further investigation of the most promising approaches. To address this gap, we provide an exhaustive examination of the current research landscape in massively MNMT, with a special emphasis on sparse models. Initially, we categorize the various sparse model-based approaches into distinct classifications. We then delve into each category in detail, elucidating their fundamental modeling principles, core issues, and the challenges they face. Wherever possible, we conduct comparative analyses to assess the strengths and weaknesses of different methodologies. Moreover, we explore potential future research avenues for MNMT based on sparse models. This survey serves as a valuable resource for both newcomers and established experts in the field of MNMT, particularly those interested in sparse model applications.