{XLT}ime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction

Anonymous

Paper Link: https://openreview.net/forum?id=6dXfj57KVdp

Paper Type: Short paper (up to four pages of content + unlimited references and appendices)

Abstract: Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference. To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages. We propose XLTime, a novel framework for multilingual TEE. XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages. XLTime alleviates problems caused by a shortage of data in the target language. We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins. XLTime also closes the gap considerably on the handcrafted HeidelTime method.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC-4

Copyright Consent Signature (type Name Or NA If Not Transferrable): Yuwei Cao

Copyright Consent Name And Address: Computer Science Department, University of Illinois Chicago, 851 S Morgan St, Chicago, IL 60607