IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News

Awasthy, Parul; Ni, Jian; Barker, Ken; Florian, Radu

doi:10.18653/v1/2021.case-1.18

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Following them, various improved architectures such as LSTM-Attention, Convolutional Recurrent Neural Network (CRNN) and CNN-Attention were proposed for sentencelevel event detection (Liu et al, 2019a;Huynh et al, 2016). Recently with the success of transformers such as BERT (Devlin et al, 2019), RoBERTa (Liu et al, 2019b), and XLM-RoBERTa (XLM-R) (Conneau et al, 2020), state-of-the-art sentence-level event detection models are based on transformers (Hu and Stoehr, 2021;Awasthy et al, 2021;Hettiarachchi et al, 2023a) which we also use in this research.…”

Section: Event Detectionmentioning

confidence: 99%

“…Later, neural network architectures such as Bidirectional LSTM (Bi-LSTM), Dynamic Multi-pooling CNNs (DMCNNs), Bi-LSTM-DMCNN and multi-attention were proposed for word-level event detection (Nguyen et al, 2016;Feng et al, 2016;Chen et al, 2015;Balali et al, 2020;Ding and Li, 2018). Very recently, similar to the sentence-level, different pre-trained transformers such as BERT and XLM-R were used at word-level Huang and Ji, 2020;Awasthy et al, 2021;Hettiarachchi et al, 2023a), setting the state-of-the-art performance (Hürriyetoglu et al, 2021a(Hürriyetoglu et al, , 2022. In summary, previous research built separate models for sentence and word-level event detection.…”

Section: Event Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Event Detection with Event Trigger Identification as Rationale Extraction

Hettiarachchi,

Ranasinghe

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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Most event detection methods act at the sentence-level and focus on identifying sentences related to a particular event. However, identifying certain parts of a sentence that act as event triggers is also important and more challenging, especially when dealing with limited training data. Previous event detection attempts have considered these two tasks separately and have developed different methods. We hypothesise that similar to humans, successful sentence-level event detection models rely on event triggers to predict sentence-level labels. By exploring feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-the-art sentence-level event detection models and show that explanations (i.e. rationales) extracted from these models can indeed be used to detect event triggers. We, therefore, (i) introduce a novel weakly-supervised method for event trigger detection; and (ii) propose to use event triggers as an explainable measure in sentence-level event detection. To the best of our knowledge, this is the first explainable machine learning approach to event trigger identification.

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Section: Event Detectionmentioning

confidence: 99%

Section: Event Detectionmentioning

confidence: 99%

Explainable Event Detection with Event Trigger Identification as Rationale Extraction

Hettiarachchi,

Ranasinghe

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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TTL: transformer-based two-phase transfer learning for cross-lingual news event detection

Hettiarachchi

Adedoyin-Olowe

Bhogal

et al. 2023

Int. J. Mach. Learn. & Cyber.

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Today, we have access to a vast data amount, especially on the internet. Online news agencies play a vital role in this data generation, but most of their data is unstructured, requiring an enormous effort to extract important information. Thus, automated intelligent event detection mechanisms are invaluable to the community. In this research, we focus on identifying event details at the sentence and token levels from news articles, considering their fine granularity. Previous research has proposed various approaches ranging from traditional machine learning to deep learning, targeting event detection at these levels. Among these approaches, transformer-based approaches performed best, utilising transformers’ transferability and context awareness, and achieved state-of-the-art results. However, they considered sentence and token level tasks as separate tasks even though their interconnections can be utilised for mutual task improvements. To fill this gap, we propose a novel learning strategy named Two-phase Transfer Learning (TTL) based on transformers, which allows the model to utilise the knowledge from a task at a particular data granularity for another task at different data granularity, and evaluate its performance in sentence and token level event detection. Also, we empirically evaluate how the event detection performance can be improved for different languages (high- and low-resource), involving monolingual and multilingual pre-trained transformers and language-based learning strategies along with the proposed learning strategy. Our findings mainly indicate the effectiveness of multilingual models in low-resource language event detection. Also, TTL can further improve model performance, depending on the involved tasks’ learning order and their relatedness concerning final predictions.

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Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report

Hürriyetoğlu

Tanev²,

Zavarella³

et al. 2022

Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events From Text (CAS

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We provide a summary of the fifth edition of the CASE workshop that is held in the scope of EMNLP 2022. The workshop consists of regular papers, two keynotes, working papers of shared task participants, and task overview papers. This workshop has been bringing together all aspects of event information collection across technical and social science fields. In addition to the progress in depth, the submission and acceptance of multimodal approaches show the widening of this interdisciplinary research topic.

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IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News

Cited by 3 publications

References 24 publications

Explainable Event Detection with Event Trigger Identification as Rationale Extraction

Explainable Event Detection with Event Trigger Identification as Rationale Extraction

TTL: transformer-based two-phase transfer learning for cross-lingual news event detection

Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report

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