Multi-linguality helps: Event-Argument Extraction for Disaster Domain in Cross-lingual and Multi-lingual setting

Zishan Ahmad, Deeksha Varshney, Asif Ekbal, Pushpak Bhattacharyya

Abstract
Automatic extraction of disaster-related events and their arguments from natural language text is vital for building a decision support system for crisis management. Event extraction from various news sources is a well-explored area for this objective. However, extracting events alone, without any context, provides only partial help for this purpose. Extracting related arguments like Time, Place, Casualties, etc., provides a complete picture of the disaster event. In this paper, we create a disaster domain dataset in Hindi by annotating disaster-related event and arguments. We also obtain equivalent datasets for Bengali and English from a collaboration. We build a multi-lingual deep learning model for argument extraction in all the three languages. We also compare our multi-lingual system with a similar baseline mono-lingual system trained for each language separately. It is observed that a single multi-lingual system is able to compensate for lack of training data, by using joint training of dataset from different languages in shared space, thus giving a better overall result.
Anthology ID:
2019.icon-1.16
Volume:
Proceedings of the 16th International Conference on Natural Language Processing
Month:
December
Year:
2019
Address:
International Institute of Information Technology, Hyderabad, India
Venue:
ICON
SIG:
Publisher:
NLP Association of India
Note:
Pages:
135–142
Language:
URL:
https://aclanthology.org/2019.icon-1.16
DOI:
Bibkey:
Cite (ACL):
Zishan Ahmad, Deeksha Varshney, Asif Ekbal, and Pushpak Bhattacharyya. 2019. Multi-linguality helps: Event-Argument Extraction for Disaster Domain in Cross-lingual and Multi-lingual setting. In Proceedings of the 16th International Conference on Natural Language Processing, pages 135–142, International Institute of Information Technology, Hyderabad, India. NLP Association of India.
Cite (Informal):
Multi-linguality helps: Event-Argument Extraction for Disaster Domain in Cross-lingual and Multi-lingual setting (Ahmad et al., ICON 2019)
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