Temporal Relation Classification in Hebrew

Abstract

Temporal Relation Classification (TRC) is a fundamental task in natural language processing (NLP) and is essential for achieving a comprehensive understanding of a natural language. Given a document containing two event mentions, the objective of this task is to discern which of the two events happened first. Existing TRC datasets predominantly consist of texts written in English. To accommodate the growing interest in relevant NLP applications for Hebrew, we introduce a new TRC dataset for Hebrew. Professional annotators labeled Hebrew documents with TRC labels, adhering to guidelines adapted from a similar project on English and with some changes required to address some unique aspects of the Hebrew language. Overall, we annotated a corpus of 28,757 words, corresponding to 7,260 pairs of events. In addition to releasing the new dataset, which can be accessed at https://github.com/ shahafp/TRC-Hebrew, we train several baseline models for TRC and report their performance. 1 Kfir Bar Consider the following Hebrew text as an example: "‫א כ ל תי א ת ה ת פו ח א ח רי ש ש ט פ תי או תו‬ "I ate the apple after washing it" The event detection subtask is about detecting only the relevant events for our domain of interest; in this example: ‫“ א כ ל ת י‬I ate” and ‫“ ש ט פ ת י‬I washed”. In this case, both words are verbs representing actions which we subsequently mark as events. In the TRC subtask, we classify each pair of events using a closed set of labels to establish their chronological order. In this example, the events ‫“ א כ ל ת י‬I ate” and ‫“ ש ט פ ת י‬I washed” should be assigned the label AFTER to indicate that “I ate” occurs after “I washed” in chronological order. This study focuses on creating a Hebrew dataset for the second subtask, TRC, which is generally defined as a classification problem involving two events accompanied by their contextual information. Utilizing the annotated data, we train multiple TRC baseline models using all available Hebrew language models.