This paper explores new frontiers in agricultural natural language processing by investigating the effectiveness of using food-related text corpora for pretraining transformer-based language models. In particular, we focus on the task of semantic matching, which involves establishing mappings between food descriptions and nutrition data. To accomplish this, we fine-tune a pre-trained transformer-based language model, AgriBERT, on this task, utilizing an external source of knowledge, such as the FoodOn ontology. To advance the field of agricultural NLP, we propose two new avenues of exploration: (1) utilizing GPT-based models as a baseline and (2) leveraging ChatGPT as an external source of knowledge. ChatGPT has shown to be a strong baseline in many NLP tasks, and we believe it has the potential to improve our model in the task of semantic matching and enhance our model's understanding of food-related concepts and relationships. Additionally, we experiment with other applications, such as cuisine prediction based on food ingredients, and expand the scope of our research to include other NLP tasks beyond semantic matching. Overall, this paper provides promising avenues for future research in this field, with potential implications for improving the performance of agricultural NLP applications.

翻译：本文通过探究基于食品语料库预训练的Transformer语言模型的有效性，探索了农业自然语言处理的新前沿。具体而言，我们聚焦于语义匹配任务，该任务涉及建立食品描述与营养数据之间的映射关系。为实现这一目标，我们在该任务上微调了一个预训练的Transformer语言模型AgriBERT，并利用外部知识源（如FoodOn本体）提升性能。为推进农业自然语言处理领域发展，我们提出两个新探索方向：(1) 使用基于GPT的模型作为基线；(2) 利用ChatGPT作为外部知识源。ChatGPT在众多自然语言处理任务中已展现出强大基线性能，我们认为其有潜力改进模型在语义匹配任务中的表现，并增强模型对食品相关概念及关系的理解。此外，我们还尝试了其他应用（如基于食材的菜系预测），并将研究范围扩展至语义匹配之外的其他自然语言处理任务。总体而言，本文为该领域的未来研究提供了有前景的方向，对提升农业自然语言处理应用的性能具有潜在意义。