Improving Zero-Shot Event Extraction via Sentence Simplification
This work addresses a specific bottleneck in event extraction for applications like geopolitical analysis, offering incremental improvements to existing MRC-based methods.
The paper tackled the problem of long-range dependencies and syntactic complexity in zero-shot event extraction using Machine Reading Comprehension (MRC), by introducing an unsupervised sentence simplification method guided by the MRC model, resulting in performance improvements of over 5% for actor extraction and over 10% for target extraction on the ICEWS dataset.
The success of sites such as ACLED and Our World in Data have demonstrated the massive utility of extracting events in structured formats from large volumes of textual data in the form of news, social media, blogs and discussion forums. Event extraction can provide a window into ongoing geopolitical crises and yield actionable intelligence. With the proliferation of large pretrained language models, Machine Reading Comprehension (MRC) has emerged as a new paradigm for event extraction in recent times. In this approach, event argument extraction is framed as an extractive question-answering task. One of the key advantages of the MRC-based approach is its ability to perform zero-shot extraction. However, the problem of long-range dependencies, i.e., large lexical distance between trigger and argument words and the difficulty of processing syntactically complex sentences plague MRC-based approaches. In this paper, we present a general approach to improve the performance of MRC-based event extraction by performing unsupervised sentence simplification guided by the MRC model itself. We evaluate our approach on the ICEWS geopolitical event extraction dataset, with specific attention to `Actor' and `Target' argument roles. We show how such context simplification can improve the performance of MRC-based event extraction by more than 5% for actor extraction and more than 10% for target extraction.