Summary of Self-training with Pseudo-label Scorer For Aspect Sentiment Quad Prediction, by Yice Zhang et al.
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
by Yice Zhang, Jie Zeng, Weiming Hu, Ziyi Wang, Shiwei Chen, Ruifeng Xu
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Aspect Sentiment Quad Prediction (ASQP) method aims to predict all quads, including aspect term, aspect category, opinion term, and sentiment polarity, for a given review. This is the most representative and challenging task in aspect-based sentiment analysis. The scarcity of labeled data limits the performance of existing methods, which is addressed by a self-training framework with a pseudo-label scorer. The scorer assesses the match between reviews and their pseudo-labels to filter out mismatches and enhance effectiveness. The quality of the training dataset and model architecture are critical aspects for ensuring the scorer’s effectiveness and reliability. The proposed approach creates a human-annotated comparison dataset and trains a generative model using ranking-based objectives. Experiments on public ASQP datasets demonstrate that the pseudo-label scorer can greatly and consistently improve self-training effectiveness. The feasibility of replacing humans with large language models for comparison dataset annotation is also explored, with promising results. The method’s code and data are released at this GitHub URL, allowing for further development and improvement of ASQP techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASQP predicts all quads (aspect term, aspect category, opinion term, sentiment polarity) in a review. This task is very important in understanding people’s opinions about products or services. The problem with predicting quads is that there isn’t enough labeled data to train machines to do it well. To solve this issue, the proposed method uses self-training and a special scorer to evaluate how well pseudo-labels match reviews. This helps get rid of mistakes and makes the training process better. The method creates a dataset for comparing reviews and trains a machine learning model using specific goals. Tests show that this approach can greatly improve quad prediction performance. The idea of having large language models do the comparison work instead of humans is also explored, with good results. |
Keywords
» Artificial intelligence » Generative model » Machine learning » Self training
