Papers by Linh Ngo
Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports (2024.lrec-main)
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| Challenge: | Existing methods for Event Causality Identification (ECI) rely on external toolkits or human annotation to obtain training signals. |
| Approach: | They propose a generative framework that leverages Optimal Transport to automatically select the most important sentences and words from full documents. |
| Outcome: | The proposed framework can predict causal relation between two events in text without external tools. |
Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
| Outcome: | The proposed method is able to induce effective graph structures for input texts to boost representation learning. |
SharpSeq: Empowering Continual Event Detection through Sharpness-Aware Sequential-task Learning (2024.naacl-long)
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| Challenge: | Existing methods for continual event detection suffer from catastrophic forgetting . a novel continual learning paradigm leveraging sharpness-aware minimization is needed . |
| Approach: | They propose a continual learning paradigm that leverages sharpness-aware minimization and a generative model to balance training data distribution. |
| Outcome: | The proposed approach outperforms existing methods on real-world datasets. |
Realistic Evaluation of Toxicity in Large Language Models (2024.findings-acl)
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| Challenge: | a large amount of data exposes large language models to toxicity and bias . prompt engineering can be easily bypassed with minimal prompt engineering. |
| Approach: | They propose a dataset that uses manually crafted prompts to nullify protective layers of large language models. |
| Outcome: | The proposed dataset shows that prompts can nullify protective layers of large language models. |
Lifelong Event Detection via Optimal Transport (2024.emnlp-main)
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| Challenge: | Continual event detection (CED) is a challenging task due to catastrophic forgetting, where learning new tasks hampers performance on previous ones. |
| Approach: | They propose a method that leverages optimal transport principles to align the optimization of a classification module with the intrinsic nature of each class, as defined by their pre-trained language modeling. |
| Outcome: | The proposed method outperforms state-of-the-art methods on MAVEN and ACE datasets and is a pioneering solution in continual event detection. |
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization (2024.findings-emnlp)
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| Challenge: | Recent advances in neural topic models have focused on two main directions: the integration of the inference network with a pre-trained language model and the modeling of the relationship between words and topics in the generative model. |
| Approach: | They propose a framework to maximize mutual information between topics and PLMs . Experimental results indicate that NeuroMax reduces inference time . |
| Outcome: | The proposed framework reduces inference time, generates more coherent topics and topic groups, and produces more representative document embeddings. |
Preserving Generalization of Language models in Few-shot Continual Relation Extraction (2024.emnlp-main)
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| Challenge: | Existing methods for Few-shot Continual Relations Extraction (FCRE) are limited in labeled training data and models must learn from a few new samples to solve new tasks. |
| Approach: | They propose a method that leverages often-discarded language model heads to integrate knowledge from new relations with limited labeled data while avoiding catastrophic forgetting. |
| Outcome: | The proposed method circumvents catastrophic forgetting and preserves prior knowledge from pre-trained backbones while maintaining accuracy of existing classifications. |
A Spectral Viewpoint on Continual Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing methods to solve the Continual Relation Extraction problem have been proposed . |
| Approach: | They propose a class-wise regularization method that preserves eigenvectors for each class shape . they propose spectral regularization to preserve eenvector shape after learning new tasks . |
| Outcome: | The proposed method improves performance on two benchmark datasets. |
Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual transfer learning for event detection are mixed with event-discriminative context. |
| Approach: | They propose a method where representations are augmented with additional context to bridge the gap between languages while enriching contextual information to facilitate ED. |
| Outcome: | The proposed model performs well on three languages. |