Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction

Information Extraction, which aims to extract structural relational triple or event from unstructured texts, often suffers from data scarcity issues. With the development of pre-trained language models, many prompt-based approaches to data-efficient information extraction have been proposed and achieved impressive performance. However, existing prompt learning methods for information extraction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structure knowledge with pre-defined schema; (ii) representation learning with locally individual instances limits the performance given the insufficient features. In this paper, we propose a novel approach of schema-aware Reference As Prompt (RAP), which dynamically leverage schema and knowledge inherited from global (few-shot) training data for each sample. Specifically, we propose a schema-aware reference store, which unifies symbolic schema and relevant textual instances. Then, we employ a dynamic reference integration module to retrieve pertinent knowledge from the datastore as prompts during training and inference. Experimental results demonstrate that RAP can be plugged into various existing models and outperforms baselines in low-resource settings on four datasets of relational triple extraction and event extraction. In addition, we provide comprehensive empirical ablations and case analysis regarding different types and scales of knowledge in order to better understand the mechanisms of RAP. Code is available in https://github.com/zjunlp/RAP.Comment: Work in progres

Low-Resource Event Extraction

The last decade has seen the extraordinary evolution of deep learning in natural language processing leading to the rapid deployment of many natural language processing applications. However, the field of event extraction did not witness a parallel success story due to the inherent challenges associated with its scalability. The task itself is much more complex than other NLP tasks due to the dependency among its subtasks. This interlocking system of tasks requires a full adaptation whenever one attempts to scale to another domain or language, which is too expensive to scale to thousands of domains and languages. This dissertation introduces a holistic method for expanding event extraction to other domains and languages within the limited available tools and resources. First, this study focuses on designing neural network architecture that enables the integration of external syntactic and graph features as well as external knowledge bases to enrich the hidden representations of the events. Second, this study presents network architecture and training methods for efficient learning under minimal supervision. Third, we created brand new multilingual corpora for event relation extraction to facilitate the research of event extraction in low-resource languages. We also introduce a language-agnostic method to tackle multilingual event relation extraction. Our extensive experiment shows the effectiveness of these methods which will significantly speed up the advance of the event extraction field. We anticipate that this research will stimulate the growth of the event detection field in unexplored domains and languages, ultimately leading to the expansion of language technologies into a more extensive range of diaspora