Event extraction is an important task in natural language processing that focuses on mining event-related information from unstructured text. Despite considerable advancements, it is still challenging to achieve satisfactory performance in this task, and issues like data scarcity and imbalance obstruct progress. In this paper, we introduce an innovative approach where we employ Large Language Models (LLMs) as expert annotators for event extraction. We strategically include sample data from the training dataset in the prompt as a reference, ensuring alignment between the data distribution of LLM-generated samples and that of the benchmark dataset. This enables us to craft an augmented dataset that complements existing benchmarks, alleviating the challenges of data imbalance and scarcity and thereby enhancing the performance of fine-tuned models. We conducted extensive experiments to validate the efficacy of our proposed method, and we believe that this approach holds great potential for propelling the development and application of more advanced and reliable event extraction systems in real-world scenarios.

Is a Large Language Model a Good Annotator for Event Extraction? | VIDEO

Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks.
However, a significant challenge that arises nowadays is how to maintain performance when we use a lightweight model with limited labeled samples. 
We present DisCo, a semi-supervised learning (SSL) framework for fine-tuning a cohort of small student models generated from a large PLM using knowledge distillation.
Our key insight is to share complementary knowledge among distilled student cohorts to promote their SSL effectiveness. 
DisCo employs a novel co-training technique to optimize a cohort of multiple small student models by promoting knowledge sharing among students under diversified views: model views produced by different distillation strategies and data views produced by various input augmentations. 
We evaluate DisCo on both semi-supervised text classification and extractive summarization tasks.
Experimental results show that DisCo can produce student models that are $7.6\times$ smaller and $4.8 \times$ faster in inference than the baseline PLMs while maintaining comparable performance.
We also show that DisCo-generated student models outperform the similar-sized models elaborately tuned in distinct tasks.

DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining

Main Track - Natural Language Processing

technical paper

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Is a Large Language Model a Good Annotator for Event Extraction? | VIDEO

DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining

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