Improving Zero-Shot Event Extraction via Sentence Simplification
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Event Argument ExtractionEvent ExtractionExtractive Question-AnsweringMachine Reading ComprehensionQuestion AnsweringReading ComprehensionSentenceZero-shot Event ExtractionSimilar Papers 제목 키워드 기반
Zero-Shot Crosslingual Sentence Simplification
Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists i…
DecoderSentenceTranslationSentence Simplification via Large Language Models
Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning. Large Language models (LLMs) have demonstrated the ability to perform a variety of natural language proc…
Few-Shot LearningSentenceA New Dataset and Empirical Study for Sentence Simplification in Chinese
Sentence Simplification is a valuable technique that can benefit language learners and children a lot. However, current research focuses more on English sentence simplification. The development of Chinese sentence simpli…
Few-Shot LearningSentenceSiTSE: Sinhala Text Simplification Dataset and Evaluation
Text Simplification is a task that has been minimally explored for low-resource languages. Consequently, there are only a few manually curated datasets. In this paper, we present a human curated sentence-level text simpl…
SentenceText SimplificationTransfer LearningAnalysing Zero-Shot Readability-Controlled Sentence Simplification
Readability-controlled text simplification (RCTS) rewrites texts to lower readability levels while preserving their meaning. RCTS models often depend on parallel corpora with readability annotations on both source and ta…
SentenceText Simplification