Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023)

Abstract: Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates.

## introduction recently, aspect sentiment quad prediction (asqp) has received extensive attention in the field of aspect-level sentiment analysis. asqp targets a comprehensive sentiment understanding and extracts four elements of aspect sentiment, including 1) aspect term (at) which is the concrete aspect description; 2) opinion term (ot) suggesting the specific opinion expression towards the aspect; 3) aspect category (ac) denoting the aspect class; 4) sentiment polarity (sp) indicating the sentiment class of the aspect. for example, given a comment sentence "service was good and food was wonderful", asqp aims to recognize two quadruples ## conclusion generation-based paradigm has become a new trend for asqp. yet previous works mainly consider what to generate but ignore what not to generate. in this work, we propose a template-agnostic uncertainty-aware unlikelihood learning (uaul) method to address negative samples. we acquire easily-mistaken samples by modeling the built-in uncertainty of language models. then based on the mistakes, we propose marginalized unlikelihood learning to promote the distinguishable of the noises and errors. to balance the impact of marginalized unlikelihood learning, we design minimization entropy. extensive experiments on various generate-based methods demonstrate uaul has universal effectiveness towards different templates. limitations our work is the first study of generative asqp task from the view of what not to generate. despite the state-of-the-art performance and template-agnostic effectiveness, our work still has limitations that may guide the direction of future work. firstly, implicit information is still challenging for uaul. failed cases in error analysis §3.3.6 demonstrate that tough cases require in-depth semantic understanding. though uaul achieves wide improvements in the generation paradigm, it struggles to deal with implicit cases. secondly, in this work, we only design tokenlevel marginalized unlikelihood learning. since aspect sentiment quadruplets contain four types of information, considering span-level and whole sequence-level negative sample learning may attain further gains. thirdly, uaul increases the training time, as shown in