Event Causality Identification (ECI) is the task of detecting causal relations between events mentioned in the text. Although this task has been extensively studied for English materials, it is under-explored for many other languages. A major reason for this issue is the lack of multilingual datasets that provide consistent annotations for event causality relations in multiple non-English languages. To address this issue, we introduce a new multilingual dataset for ECI, called MECI. The dataset employs consistent annotation guidelines for five typologically different languages, i.e., English, Danish, Spanish, Turkish, and Urdu. Our dataset thus enable a new research direction on cross-lingual transfer learning for ECI. Our extensive experiments demonstrate high quality for MECI that can provide ample research challenges and directions for future research. We will publicly release MECI to promote research on multilingual ECI.

Extracting entities, events, event arguments, and relations (i.e., task instances) from text represents four main challenging tasks in information extraction (IE), which have been solved jointly (JointIE) to boost the overall performance for IE.As such, previous work often leverages two types of dependencies between the tasks, i.e., cross-instance and cross-type dependencies representing relatedness between task instances and correlations between information types of the tasks.However, the crosstask dependencies in prior work are not optimal as they are only designed manually according to some task heuristics.To address this issue, we propose a novel model for JointIE that aims to learn cross-task dependencies from data.In particular, we treat each task instance as a node in a dependency graph where edges between the instances are inferred through information from different layers of a pretrained language model (e.g., BERT).Furthermore, we utilize the Chow-Liu algorithm to learn a dependency tree between information types for JointIE by seeking to approximate the joint distribution of the types from data.Finally, the Chow-Liu dependency tree is used to generate cross-type patterns, serving as anchor knowledge to guide the learning of representations and dependencies between instances for JointIE.Experimental results show that our proposed model significantly outperforms strong JointIE baselines over four datasets with different languages.

Luis Guzman-Nateras, Minh Van Nguyen, Thien Nguyen. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Minh Van Nguyen, Bonan Min, Franck Dernoncourt, Thien Nguyen. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Nguyen. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data.An approach to overcome this issue is to use unlabeled data in the target language to aid the alignment of crosslingual representations, i.e., via fooling a language discriminator.However, as this approach does not condition on class information, a target language example of a class could be incorrectly aligned to a source language example of a different class.To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning.In particular, we propose to learn two versions of representation vectors for each class in an REE task based on either source or target language examples.Representation vectors for corresponding classes will then be aligned to achieve class-aware alignment for crosslingual representations.In addition, we propose to further align representation vectors for languageuniversal word categories (i.e., parts of speech and dependency relations).As such, a novel filtering mechanism is presented to facilitate the learning of word category representations from contextualized representations on input texts based on adversarial learning.We conduct extensive crosslingual experiments with English, Chinese, and Arabic over REE tasks.The results demonstrate the benefits of the proposed method that significantly advances the state-of-the-art performance in these settings.

Event Detection (ED) aims to recognize and classify trigger words of events in text.The recent progress has featured advanced transformer-based language models (e.g., BERT) as a critical component in stateof-the-art models for ED.However, the length limit for input texts is a barrier for such ED models as they cannot encode long-range document-level context that has been shown to be beneficial for ED.To address this issue, we propose a novel method to model documentlevel context with BERT for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence.The target sentence will be then augmented with the selected sentences and consumed entirely by BERT for improved representation learning for ED.To this end, the RE-INFORCE algorithm is employed to train the relevant sentence selection for ED.Several information types are then introduced to form the reward function for the training process, including ED performance, sentence similarity, and discourse relations.Our extensive experiments on multiple benchmark datasets reveal the effectiveness of the proposed model, leading to new state-of-the-art performance.

Existing works on information extraction (IE)have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks.This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE).Compared to few prior work on jointly performing four IE tasks, FourIE features two novel contributions to capture inter-dependencies between tasks.First, at the representation level, we introduce an interaction graph between instances of the four tasks that is used to enrich the prediction representation for one instance with those from related instances of other tasks.Second, at the label level, we propose a dependency graph for the information types in the four IE tasks that captures the connections between the types expressed in an input sentence.A new regularization mechanism is introduced to enforce the consistency between the golden and predicted type dependency graphs to improve representation learning.We show that the proposed model achieves the state-of-the-art performance for joint IE on both monolingual and multilingual learning settings with three different languages.

We address the poor generalization of few-shot learning models for event detection (ED) using transfer learning and representation regularization. In particular, we propose to transfer knowledge from open-domain word sense disambiguation into few-shot learning models for ED to improve their generalization to new event types. We also propose a novel training signal derived from dependency graphs to regularize the representation learning for ED. Moreover, we evaluate few-shot learning models for ED with a large-scale human-annotated ED dataset to obtain more reliable insights for this problem. Our comprehensive experiments demonstrate that the proposed model outperforms state-of-the-art baseline models in the few-shot learning and supervised learning settings for ED. Code and data splits are available at https://github.com/laiviet/ed-fsl.