What is it about?
This paper presents a new approach to learning with limited examples, focusing on understanding the stance expressed in conversations. For instance, it might help determine how someone feels about a statement in a discussion with various viewpoints. The method uses the ongoing conversation itself to generate prompts for learning from just a few examples, reducing the need for extensive training data. Early findings indicate that this approach could be as effective as traditional methods while being more cost-efficient to develop.
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Why is it important?
Stance detection is important in natural language processing (NLP) because it helps understand the attitudes, beliefs, or perspectives expressed in text data. It can be applied to different problems: Sentiment Analysis Enhancement, Contextual Understanding, Decision-Making and Information Retrieval, Debate and Argumentation Analysis, and Fake News Detection.
Perspectives
The present work leaves several opportunities open to investigation. First, we notice that the current model may be further assessed using the extended RumourEval 2019 dataset, or other similar resources. Second, we may consider alternative prompt engineering methods including, for instance, enriching the prompt instructions with external knowledge about the conversation topic (e.g., from news articles, Wikipedia, etc.) Moreover, the present approach may in principle be applied to other text classification tasks based on contextual information including, for instance, sarcasm or sentiment detection.
Dr. Luciano Digiampietri
Universidade de Sao Paulo Campus da Capital
Read the Original
This page is a summary of: Contextual stance classification using prompt engineering, September 2023, Comissao Especial de Informatica na Educacao,
DOI: 10.5753/stil.2023.233708.
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