To address the deficiencies in chart types and the limited scope of chart tasks in existing datasets, we conducted a comprehensive review of current data collection methodologies. By integrating manual annotation with data generation leveraging GPT-4, we developed a dataset that includes 21 diverse chart types and a broad spectrum of tasks, such as data retrieval and mathematical reasoning. Our analysis of existing models revealed that capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types are essential for performing a variety of chart tasks. To overcome the limitations in these areas, we devised a two-stage training strategy and a method for jointly training the vision encoder tailored for multi-type charts. In the first stage, we designed several tasks to enhance the model’s general understanding of charts, aligning multimodal large models pre-trained on natural images to chart tasks. To further improve the model’s capability to understand various chart tasks and enhance its reasoning abilities, we employed Chain-of-Thought data for training in the second stage. Through two-stage training on our proposed dataset, the pre-trained multimodal large language model achieved state-of-the-art performance across multiple chart understanding tasks, demonstrating the superiority of our data and methods.

We tackle Event Argument Extraction (EAE) in the manner of template-based generation. Based on our exploration of generative EAE, it suffers from several issues, such as multiple arguments of one role, generating words out of context and inconsistency with prescribed format. We attribute it to the weakness of following complex input prompts. To address these problems, we propose the demonstration retrieval-augmented generative EAE (DRAGEAE), containing two components: event knowledge-injected generator (EKG) and demonstration retriever (DR). EKG employs event knowledge prompts to capture role dependencies and semantics. DR aims to search informative demonstrations from training data, facilitating the conditional generation of EKG. To train DR, we use the probability-based rankings from large language models (LLMs) as supervised signals. Experimental results on ACE-2005, RAMS and WIKIEVENTS demonstrate that our method outperforms all strong baselines and it can be generalized to various datasets. Further analysis is conducted to discuss the impact of diverse LLMs and prove that our model alleviates the above issues.

In Visually-rich Document Understanding (VrDU), recent advances of incorporating layout and image features into the pre-training language models have achieved significant progress. Existing methods usually developed complicated dedicated architectures based on pre-trained models and fine-tuned them with costly high-quality data to eliminate the inconsistency of knowledge distribution between the pre-training task and specialized downstream tasks. However, due to their huge data demands, these methods are not suitable for few-shot settings, which are essential for quick applications with limited resources but few previous works are presented. To solve these problems, we propose a unified Knowledge-aware prompt-tuning framework for Visual-rich Document Understanding (KnowVrDU) to enable broad utilization for diverse concrete applications and reduce data requirements. To model heterogeneous VrDU structures without designing task-specific architectures, we propose to reformulate various VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. To bridge the knowledge gap between the pre-training task and specialized VrDU tasks without additional annotations, we propose a prompt knowledge integration mechanism to leverage external open-source knowledge bases. We conduct experiments on several benchmark datasets in few-shot settings and the results validate the effectiveness of our method.

Event Detection (ED) is a task of automatically extracting multi-class trigger words. The understanding of word sense is crucial for ED. In this paper, we utilize context-specific commonsense knowledge to strengthen word sense modeling. Specifically, we leverage a Context-specific Knowledge Selector (CKS) to select the exact commonsense knowledge of words from a large knowledge base, i.e., ConceptNet. Context-specific selection is made in terms of the relevance of knowledge to the living contexts. On this basis, we incorporate the commonsense knowledge into the word-level representations before decoding. ChatGPT is an ideal generative CKS when the prompts are deliberately designed, though it is cost-prohibitive. To avoid the heavy reliance on ChatGPT, we train an offline CKS using the predictions of ChatGPT over a small number of examples (about 9% of all). We experiment on the benchmark ACE-2005 dataset. The test results show that our approach yields substantial improvements compared to the BERT baseline, achieving the F1-score of about 78.3%. All models, source codes and data will be made publicly available.