The Second Workshop on Knowledge-Augmented Methods for Natural Language Processing

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Abstract

Language models are being developed and deployed in many applications, "small"-scale and large-scale, generic and specialized, text-only and multimodal, etc. Meanwhile, the missingness of important knowledge causes limitations and safety challenges. The knowledge includes commonsense, world facts, domain expertise, personalization, and especially the unique patterns that need to be discovered from big data applications. Training and inference processes of the language models can be and should be augmented with the knowledge. The first KnowledgeNLP at AAAI 2023 attracted scientists on knowledge augmentation methods towards higher language intelligence. This workshop offers a broad platform to share ideas and discuss various topics, such as (1) synergy between knowledge and language model, (2) scalable architectures that integrate NLP, knowledge graph, and graph learning technologies, (3) KnowledgeNLP for e-commerce, education, and healthcare, (4) human factors and social good in KnowledgeNLP.

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Yu, W., Tong, L., Shi, W., Peng, N., & Jiang, M. (2023). The Second Workshop on Knowledge-Augmented Methods for Natural Language Processing. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 5899–5900). Association for Computing Machinery. https://doi.org/10.1145/3580305.3599233

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