M v P : Multi-view Prompting Improves Aspect Sentiment Tuple Prediction (2023)

Abstract: Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity of language expression on the results. In this work, we propose Multi-view Prompting (MVP) that aggregates sentiment elements generated in different orders, leveraging the intuition of human-like problem-solving processes from different views. Specifically, MVP introduces element order prompts to guide the language model to generate multiple sentiment tuples, each with a different element order, and then selects the most reasonable tuples by voting. MVP can naturally model multi-view and multi-task as permutations and combinations of elements, respectively, outperforming previous task-specific designed methods on multiple ABSA tasks with a single model. Extensive experiments show that MVP significantly advances the state-of-the-art performance on 10 datasets of 4 benchmark tasks, and performs quite effectively in low-resource settings. Detailed evaluation verified the effectiveness, flexibility, and cross-task transferability of MVP.

## introduction aspect-based sentiment analysis (absa) aims to predict tuples of sentiment elements of interest for a given text. there are four sentiment elements that constitute the main line of absa research: aspect term (a), aspect category (c), opinion term (o) and sentiment polarity (s) @xcite . given an example sentence, "i love the sushi badly!", the corresponding elements are "sushi", "food quality", task output aspect category opinion sentiment (acos) (a, c, o, s) aspect sentiment quad prediction (asqp) (a, c, o, s) aspect sentiment triplet extraction (aste) (a, o, s) target aspect sentiment detection (tasd) recently, generative methods have been used to handle various absa tasks uniformly and achieved good performance @xcite , where the common practice is to generate a sequence of sentiment elements in a specified format to leverage label semantics. to be specific, they use class index @xcite , sentiment element sequence @xcite , natural language @xcite , structured extraction schema @xcite or opinion tree @xcite as the target of the generation models. however, previous works usually generate the sequence of sentiment elements in a left-to-right fixed order, which ignores the influence of the interdependence of the elements in a sentiment tuple single-order prediction multi-view prompting mvp ① element order-based prompting ③ aggregation ② multi-view tuple prediction i love the sushi badly! lm ✓ ✓ (sushi, food, love, pos) the food is bad because sushi is badly i love the sushi badly! paraphrase recover (sushi, food, badly, neg) ✘ ✓ [c] [o] [a] [s] [c]food [o]love [s]great [a]sushi [o] [a] [c] [s] [o]badly [a]sushi [c]food [s]bad [o] [s] [c] [a] [a]sushi [c]food [o]love [s]great ## methodology to better understand the operation process of the proposed mvp, we can carefully observe the pipeline shown in figure 1 . unlike the fixed order element prediction adopted by previous methods like paraphrase @xcite , we take mvp acos: i'll be back, i love the sushi badly! [o] [s] [c] [a] [a] sushi [c] food [o] love [a] it [c] rest [s] great [o] back [s] great [ssep] [a] sushi [s] great [c] food [a] sushi [c] food [o] love [s] [c] [a] tasd: i'll be back, i love the sushi badly! [o] [c] [a] aste: i'll be back, i love the sushi badly! every possible permutation of sentiment elements (6 for the triplet and 24 for the quadruplet) into account and select the appropriate subsets of them for efficiency and effectiveness reasons. conditioned on different ordered prompts, a model can generate multiple tuples from different views. some views give the same correct tuples, while some views are less effective and thus might be wrong, but it's unlikely to result in the same error. in other words, different views tend to show more agreement in the correct sentiment tuples. following this intuition, the proposed mvp aggregates and takes the tuples that most views agree on as the final result. ## conclusion in this work, we introduce an element order-based prompt learning method -mvp, which improves aspect-level opinion information prediction by simple yet effective multi-view results aggregation. leveraging the intuition of solving problems from different views, mvp advances the research of generative modeling for tuple structure prediction. by combining and permuting the sentiment elements, our multi-tasking model substantially outperforms task-specific models on a variety of absa tasks. detailed experiments show that our method significantly advances the state-of-the-art on benchmark datasets, in both supervised and low-resource settings. we hope our research will shed light on generative tuple prediction.