Papers by Minh Van Nguyen
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing (2021.eacl-demos)
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| Challenge: | Trankit is a lightweight, pre-trained toolkit for multilingual natural language processing. |
| Approach: | They propose a transformer-based toolkit for multilingual natural language processing that trains pipelines over 100 languages and 90 pretrained pipelines for 56 languages. |
| Outcome: | The proposed tool outperforms existing pipelines over sentence segmentation, part-of-speech tagging, morphological feature tabbing, and dependency parsing while maintaining competitive performance over tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. |
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction (2022.naacl-demo)
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| Challenge: | Existing active learning frameworks require long time between annotation batches due to time-consuming nature of model training and data selection. |
| Approach: | They propose a small proxy network to synchronize the proxy network with the main large model to ensure appropriateness of the selected annotation examples for the main model. |
| Outcome: | The proposed framework can support multiple languages and is available on github and demo website. |
Document-Level Event Argument Extraction via Optimal Transport (2022.findings-acl)
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| Challenge: | Prior work on event-level EAE models ignore syntactic structures for documents . prior work on EE is restricted to sentence-level setting where event triggers and arguments are assumed to appear in the same sentences. |
| Approach: | They propose to employ Optimal Transport to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. |
| Outcome: | The proposed model is effective in document-level EAE, with a new constraint on unrelated context words. |
Event Extraction from Historical Texts: A New Dataset for Black Rebellions (2021.findings-acl)
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| Challenge: | Using EE to extract historical data, we study the discourse of slave and non-slave African diaspora rebellions published in the periodical press in this period. |
| Approach: | They propose a dataset to detect event trigger words and their arguments in nineteenth-century newspapers. |
| Outcome: | The proposed dataset features 5 entity types, 12 event types, and 6 argument roles that concern slavery and black movements between the eighteenth and nineteenth centuries. |
MECI: A Multilingual Dataset for Event Causality Identification (2022.coling-1)
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| Challenge: | Event Causality Identification (ECI) is a task of detecting causal relations between events mentioned in text. |
| Approach: | They propose a multilingual dataset that provides consistent annotations for event causality relations in five languages. |
| Outcome: | The proposed dataset provides consistent annotation guidelines for five languages . the dataset can provide ample research challenges and directions for future research . |
MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection (2022.naacl-main)
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| Challenge: | Existing methods for ED in IE and NLP focus on feature-based models to feature-driven models. |
| Approach: | They propose to use a multilingual dataset to annotate events for 8 different languages . they demonstrate the challenges and transferability of ED across languages in MINION . |
| Outcome: | a new dataset that consistently annotates events for 8 different languages is released . the new dataset will promote future research on multilingual ED . |
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies (2022.naacl-main)
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| Challenge: | Existing models that perform information extraction tasks manually assume heuristic dependency between the task instances and mean-field factorization for the joint distribution of instance labels. |
| Approach: | They propose to induce a dependency graph among task instances to boost representation learning by estimating their joint distribution via Conditional Random Fields. |
| Outcome: | The proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages. |
Learning Cross-Task Dependencies for Joint Extraction of Entities, Events, Event Arguments, and Relations (2022.emnlp-main)
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| Challenge: | Existing work on IE tasks that use two types of dependencies is not optimal . emr, event trigger detection, event argument extraction, and relation extraction are challenging . |
| Approach: | They propose a model that learns cross-task dependencies from data . they treat each task instance as a node in a dependency graph . |
| Outcome: | The proposed model outperforms strong baselines over four datasets with different languages. |
Cross-Lingual Event Detection via Optimized Adversarial Training (2022.naacl-main)
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| Challenge: | Recent work in this area has harnessed the language-invariant qualities of pre-trained Multi-lingual Language Models. |
| Approach: | They propose to use adversarial language adaptation to train a model to detect events in a target language. |
| Outcome: | The proposed model achieves state-of-the-art on 8 different language pairs, using 4 languages from unrelated families. |
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks (2021.naacl-main)
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| Challenge: | Existing work on information extraction (IE) has solved the four main tasks separately, thus failing to benefit from inter-dependencies between tasks. |
| Approach: | They propose a model to solve four IE tasks in a single model that captures inter-dependencies between tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance on monolingual and multilingual learning settings with three different languages. |
Modeling Document-Level Context for Event Detection via Important Context Selection (2021.emnlp-main)
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| Challenge: | Existing methods for Event Detection (ED) do not encode long-range document-level context . e.g., BERT cannot encode long text-level contextual information . |
| Approach: | They propose a method to model document-level context for Event Detection using transformer-based language models. |
| Outcome: | The proposed model can predict event prediction of target sentence in document-level context . the proposed model is effective on multiple benchmark datasets . |
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments (2021.emnlp-main)
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| Challenge: | Existing approaches to crosslingual Relation and Event Extraction (REE) suffer from monolingual bias due to training of models on source language data. |
| Approach: | They propose to use unlabeled data in target language to aid alignment of crosslingual representations by fooling a language discriminator. |
| Outcome: | The proposed method significantly advances the state-of-the-art in crosslingual REE tasks. |