UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning
Zengzhi Wang, Rui Xia, Jianfei Yu

TL;DR
UnifiedABSA introduces a multi-task instruction tuning framework that models various aspect-based sentiment analysis tasks simultaneously, improving performance and data efficiency over traditional task-specific models.
Contribution
It proposes a unified ABSA framework using multi-task instruction tuning to model diverse tasks and their inter-dependencies within a single system.
Findings
Outperforms dedicated models on 11 ABSA tasks
Demonstrates superior data efficiency
Effective in modeling inter-task dependencies
Abstract
Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. However, application scenarios of ABSA tasks are often diverse. This solution usually requires a large amount of labeled data from each task to perform excellently. These dedicated models are separately trained and separately predicted, ignoring the relationship between tasks. To tackle these issues, we present UnifiedABSA, a general-purpose ABSA framework based on multi-task instruction tuning, which can uniformly model various tasks and capture the inter-task dependency with multi-task learning. Extensive experiments on two benchmark datasets show that UnifiedABSA can significantly outperform dedicated models on 11 ABSA tasks and show its superiority in terms of data…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Web Data Mining and Analysis
MethodsByte Pair Encoding · Linear Layer · Gated Linear Unit · Refunds@Expedia|||How do I get a full refund from Expedia? · Inverse Square Root Schedule · Adafactor · Dense Connections · Softmax · Attention Dropout · Dropout
