ConKgPrompt: Enhancing Text Classification through Knowledge-Guided Prompt Learning

By Aryaman PattnayakReviewed by Susha Cheriyedath, M.Sc.Aug 31 2023

A new study published in Electronics introduces ConKgPrompt, an innovative prompt learning framework that integrates external knowledge to construct high-quality prompt verbalizers. It utilizes knowledge bases to expand labels to multiple related concepts and extract text keywords.

Text classification aims to assign categories to text based on its content. This enables automated tagging, sentiment analysis, spam detection, and more. While pre-trained language models like BERT have advanced text classification, significant challenges remain. Model size increases fine-tuning costs, and the gap between pre-training objectives and downstream tasks limits performance.

The Challenge

Prompt-based learning offers a promising paradigm to unleash the full potential of pre-trained models on specific tasks. Prompts reformulate the task as cloze-style phrase completion, avoiding extensive fine-tuning. However, constructing effective prompt verbalizers that map labels to vocabulary words remains challenging, especially for tasks where text semantics are crucial.

ConKgPrompt introduces innovations in prompt engineering to overcome these limitations. It utilizes external knowledge bases to expand label words to multiple related concepts. Text keywords are also extracted and expanded via knowledge retrieval. Word embeddings are then leveraged to refine this expanded vocabulary and select the most relevant words for each label. This produces high-quality verbalizers that integrate correlations between text and labels at differing granularities. The verbalizers transform classification into predicting the most relevant word for each input text. ConKgPrompt proposes templates to formulate the texts into cloze-style prompts for the language model.

Boosting Representation Quality

ConKgPrompt further integrates supervised contrastive learning to enhance representation quality. Contrastive objectives maximize similarities between embeddings of samples from the same class while pushing apart different classes.

A batch provides positive pairs from identical classes and negative pairs from differing classes. The contrastive module trains embeddings to cluster based on class, increasing inter-class separability. Cross-entropy loss is also incorporated to improve generalization.

Comprehensive Evaluations

The authors validate ConKgPrompt extensively on Chinese text classification datasets, outperforming previous methods in limited supervised data settings. Knowledge-guided prompting proves highly effective, and contrastive learning provides a further boost.

Quantitative results show ConKgPrompt surpasses representative baselines, including PET, P-tuning, and label-expanded prompting methods. ConKgPrompt demonstrates strong robustness across tasks and low variance, especially with sufficient labeled data.

Qualitative visualizations illustrate how contrastive learning derives more discriminative embeddings. Analyses also highlight how knowledge-based verbalizer construction captures nuanced label correlations neglected by manual or strictly label-based expansion.

While evaluated on Chinese text datasets, adapting ConKgPrompt to other languages could broaden its impact. Tailoring prompts and contrastive objectives to different linguistic typologies and scripts poses intriguing challenges. Applications in domains like biomedical and scientific text classification also warrant exploration. Domain-specific ontologies and taxonomies can provide structured knowledge for prompt engineering.

Real-world deployments should examine tuning computational efficiency and memory usage for practical usage. Quantifying model uncertainty and interpretability is another valuable direction when applying ConKgPrompt in impactful scenarios. Advancing the framework to drive pragmatic impact across languages, domains, and applications remains an exciting open frontier.

Exploring Multimodal Extensions

While ConKgPrompt focuses on text classification, the framework could be extended to multimodal scenarios. Multimodal learning integrating different data types like text, images, and audio has become increasingly prominent. Adapting ConKgPrompt to multimodal tasks could provide similar benefits in tailored prompting and representation learning. For example, image captions could be formulated as cloze-style prompts predicting relevant words from an object's vocabulary.

Knowledge bases could provide contextual visual and textual concepts to expand this vocabulary. Contrastive objectives can derive multimodal embeddings, maximizing intra-class similarity across modalities. Multimodal ConKgPrompt could support diverse applications like caption generation, visual question answering, and multimedia retrieval. Unique prompts could be constructed by synchronizing linguistic and visual conceptual spaces. Further research is needed to formulate effective multimodal prompts and adapt contrastive techniques. However, the ConKgPrompt framework offers a promising starting point for extending knowledge-enhanced prompting to multimodal domains.

Future Outlook

 ConKgPrompt could improve flexibility across diverse tasks and generalization to unseen data. Future work may investigate dynamically generating prompt templates tailored to each input text. Contrastive pre-training objectives could also help build universally valuable representations.

By integrating external knowledge and contrastive learning, ConKgPrompt advances prompt-based text classification. Its innovations accurately tailor prompt engineering to task semantics while enhancing representation learning. This work helps overcome critical challenges in applying pre-trained language models to practical classification scenarios.