no code implementations • NAACL (TextGraphs) 2021 • Duy Phung, Tuan Ngo Nguyen, Thien Huu Nguyen
Prior work has demonstrated the benefits of the predicate-argument information and document context for resolving the coreference of event mentions.
no code implementations • COLING 2022 • Nghia Ngo Trung, Linh Ngo Van, Thien Huu Nguyen
A shift in data distribution can have a significant impact on performance of a text classification model.
no code implementations • Findings (EMNLP) 2021 • Qiuhao Lu, Dejing Dou, Thien Huu Nguyen
These knowledge adapters are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller to enrich PLMs.
no code implementations • EMNLP (MRL) 2021 • Duy Phung, Hieu Minh Tran, Minh Van Nguyen, Thien Huu Nguyen
We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages.
1 code implementation • COLING 2022 • Viet Dac Lai, Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen
Our dataset thus enable a new research direction on cross-lingual transfer learning for ECI.
no code implementations • WNUT (ACL) 2021 • Duong Le, Thien Huu Nguyen
In this work, we show that the multi-hop paths between the words are also necessary to compute the sentence structures for EFP.
no code implementations • WNUT (ACL) 2021 • Minh Tran Phu, Minh Van Nguyen, Thien Huu Nguyen
In this work, we propose to fill this gap by introducing novel methods to integrate the syntactic structures into the deep learning models for FineTempRel.
no code implementations • COLING 2022 • Amir Pouran Ben Veyseh, Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
As such, the challenges of EE in informal and noisy texts are not adequately studied.
no code implementations • EMNLP 2020 • Hieu Man Duc Trong, Duc Trong Le, Amir Pouran Ben Veyseh, Thuat Nguyen, Thien Huu Nguyen
Detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text.
no code implementations • EMNLP 2020 • Viet Dac Lai, Tuan Ngo Nguyen, Thien Huu Nguyen
Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolution neural networks (GCN) to achieve state-of-the-art performance.
1 code implementation • EMNLP 2021 • Viet Lai, Franck Dernoncourt, Thien Huu Nguyen
We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction.
no code implementations • EMNLP 2021 • Amir Pouran Ben Veyseh, Minh Van Nguyen, Nghia Ngo Trung, Bonan Min, Thien Huu Nguyen
To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence.
no code implementations • EMNLP 2021 • Minh Van Nguyen, Tuan Ngo Nguyen, Bonan Min, Thien Huu Nguyen
To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning.
no code implementations • EACL (WANLP) 2021 • Minh Van Nguyen, Thien Huu Nguyen
Previous work on CEAE has shown the cross-lingual benefits of universal dependency trees in capturing shared syntactic structures of sentences across languages.
no code implementations • COLING 2022 • Amir Pouran Ben Veyseh, Quan Hung Tran, Seunghyun Yoon, Varun Manjunatha, Hanieh Deilamsalehy, Rajiv Jain, Trung Bui, Walter W. Chang, Franck Dernoncourt, Thien Huu Nguyen
To this end, this work studies new challenges of KP in transcripts of videos, an understudied domain for KP that involves informal texts and non-cohesive presentation styles.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 17 Sep 2023 • Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, Hieu Man, Nghia Trung Ngo, Franck Dernoncourt, Ryan A. Rossi, Thien Huu Nguyen
However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed.
2 code implementations • 29 Jul 2023 • Viet Dac Lai, Chien Van Nguyen, Nghia Trung Ngo, Thuat Nguyen, Franck Dernoncourt, Ryan A. Rossi, Thien Huu Nguyen
Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
no code implementations • 24 Jul 2023 • Viet Dac Lai, Abel Salinas, Hao Tan, Trung Bui, Quan Tran, Seunghyun Yoon, Hanieh Deilamsalehy, Franck Dernoncourt, Thien Huu Nguyen
Punctuation restoration is an important task in automatic speech recognition (ASR) which aim to restore the syntactic structure of generated ASR texts to improve readability.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 3 Jun 2023 • Minh Van Nguyen, Kishan Kc, Toan Nguyen, Thien Huu Nguyen, Ankit Chadha, Thuy Vu
In this paper, we propose to improve the candidate scoring by explicitly incorporating the dependencies between question-context and answer-context into the final representation of a candidate.
no code implementations • 12 Apr 2023 • Viet Dac Lai, Nghia Trung Ngo, Amir Pouran Ben Veyseh, Hieu Man, Franck Dernoncourt, Trung Bui, Thien Huu Nguyen
The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i. e., beyond reported anecdotes), which is still missing or limited in current research.
no code implementations • 13 Nov 2022 • Qiuhao Lu, Dejing Dou, Thien Huu Nguyen
Deep learning models have demonstrated superior performance in various healthcare applications.
no code implementations • 11 Nov 2022 • Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, Thien Huu Nguyen
Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i. e., participants) from text.
no code implementations • NAACL 2022 • Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen
Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text.
no code implementations • 11 Sep 2022 • Amir Pouran Ben Veyseh, Nicole Meister, Franck Dernoncourt, Thien Huu Nguyen
Keyphrase extraction is one of the essential tasks for document understanding in NLP.
no code implementations • 11 Sep 2022 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
In order to alleviate this issue, one solution is to link the streaming videos with the relevant tutorial available for the tools used in the streaming video.
no code implementations • 26 Apr 2022 • Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
Mathematical symbols and descriptions appear in various forms across document section boundaries without explicit markup.
no code implementations • COLING 2022 • Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon, Rajiv Jain, Franck Dernoncourt, Thien Huu Nguyen
Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications.
no code implementations • 19 Feb 2022 • Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
1 code implementation • NAACL (ACL) 2022 • Minh Van Nguyen, Nghia Trung Ngo, Bonan Min, Thien Huu Nguyen
FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration.
no code implementations • 19 Dec 2021 • Qiuhao Lu, Thien Huu Nguyen, Dejing Dou
Unplanned intensive care unit (ICU) readmission rate is an important metric for evaluating the quality of hospital care.
no code implementations • 1 Nov 2021 • Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, Dan Roth
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.
no code implementations • ACL 2021 • Hieu Minh Tran, Duy Phung, Thien Huu Nguyen
In addition, consistency constraints between golden and predicted clusters of event mentions have not been considered to improve representation learning in prior deep learning models for ECR.
no code implementations • ACL 2021 • Amir Pouran Ben Veyseh, Viet Lai, Franck Dernoncourt, Thien Huu Nguyen
To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data.
no code implementations • SEMEVAL 2021 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities, and conditions mentioned in a document.
1 code implementation • CVPR 2021 • Nguyen Nguyen, Thu Nguyen, Vinh Tran, Minh-Triet Tran, Thanh Duc Ngo, Thien Huu Nguyen, Minh Hoai
Language prior plays an important role in the way humans perceive and recognize text in the wild.
no code implementations • NAACL 2021 • Minh Tran Phu, Thien Huu Nguyen
Although deep learning models have recently shown state-of-the-art performance for ECI, they are limited to the intra-sentence setting where event mention pairs are presented in the same sentences.
no code implementations • EACL 2021 • Duong Le, Thien Huu Nguyen
Most of the previous work on Event Detection (ED) has only considered the datasets with a small number of event types (i. e., up to 38 types).
no code implementations • NAACL 2021 • Minh Van Nguyen, Viet Dac Lai, Thien Huu Nguyen
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.
1 code implementation • EACL 2021 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, Thien Huu Nguyen
However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available.
1 code implementation • EACL 2021 • Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, Thien Huu Nguyen
Finally, we create a demo video for Trankit at: https://youtu. be/q0KGP3zGjGc.
Ranked #1 on Sentence segmentation on UD2.5 test
no code implementations • 22 Dec 2020 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang, Leo Anthony Celi
To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively.
1 code implementation • COLING 2020 • Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
Network representation learning (NRL) is crucial in the area of graph learning.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Thu Nguyen, Duy Phung, Minh Hoai, Thien Huu Nguyen
Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Hieu Minh Tran, Minh Trung Nguyen, Thien Huu Nguyen
However, this model does not capture the representations for the nodes in the graphs, thus preventing it from effectively encoding the specific and relevant information of the nodes for DRE.
Document-level Relation Extraction Representation Learning +1
2 code implementations • COLING 2020 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Quan Hung Tran, Thien Huu Nguyen
The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.
no code implementations • 27 Oct 2020 • Viet Dac Lai, Tuan Ngo Nguyen, Thien Huu Nguyen
Recent studies on event detection (ED) haveshown that the syntactic dependency graph canbe employed in graph convolution neural net-works (GCN) to achieve state-of-the-art per-formance.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Amir Pouran Ben Veyseh, Nasim Nour, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen
In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • Findings of the Association for Computational Linguistics 2020 • Amir Pouran Ben Veyseh, Tuan Ngo Nguyen, Thien Huu Nguyen
The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word.
no code implementations • EMNLP 2020 • Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen
In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words.
Aspect-Based Sentiment Analysis Aspect-oriented Opinion Extraction +1
no code implementations • ACL 2020 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen
In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization.
1 code implementation • WS 2020 • Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types.
no code implementations • 13 Feb 2020 • Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
The existing event classification (EC) work primarily focuseson the traditional supervised learning setting in which models are unableto extract event mentions of new/unseen event types.
1 code implementation • 5 Nov 2019 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen
In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies.
no code implementations • WS 2020 • Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen
To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i. e., representation level and task-specific (i. e., label) level.
Ranked #5 on Intent Detection on SNIPS
no code implementations • WS 2019 • Tuan Ngo Nguyen, Franck Dernoncourt, Thien Huu Nguyen
Deep learning models have achieved state-of-the-art performances on many relation extraction datasets.
no code implementations • WS 2019 • Viet Dac Lai, Thien Huu Nguyen
We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new formulation.
no code implementations • 25 Sep 2019 • Xiao Zhang, Song Wang, Dejing Dou, Xien Liu, Thien Huu Nguyen, Ji Wu
Contextual representation models like BERT have achieved state-of-the-art performance on a diverse range of NLP tasks.
1 code implementation • ACL 2019 • Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively.
no code implementations • 7 Jul 2019 • Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees.
no code implementations • ACL 2019 • Linh The Nguyen, Linh Van Ngo, Khoat Than, Thien Huu Nguyen
It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR).
no code implementations • 1 Dec 2018 • Trung Minh Nguyen, Thien Huu Nguyen
The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators.
2 code implementations • ICLR 2019 • Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville
Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated.
no code implementations • WS 2018 • Lisheng Fu, Bonan Min, Thien Huu Nguyen, Ralph Grishman
Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema.
6 code implementations • ICLR 2019 • Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts.
no code implementations • EMNLP 2018 • Weiyi Lu, Thien Huu Nguyen
Event detection (ED) and word sense disambiguation (WSD) are two similar tasks in that they both involve identifying the classes (i. e. event types or word senses) of some word in a given sentence.
no code implementations • COLING 2018 • Minh Nguyen, Thien Huu Nguyen
The early work in this field \cite{keith2017identifying} proposed a distant supervision framework based on Expectation Maximization (EM) to deal with the multiple appearances of the names in documents.
no code implementations • 3 May 2018 • Minh Nguyen, Toan Nguyen, Thien Huu Nguyen
Anti-phishing aims to detect phishing content/documents in a pool of textual data.
no code implementations • IJCNLP 2017 • Lisheng Fu, Thien Huu Nguyen, Bonan Min, Ralph Grishman
Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier.
no code implementations • COLING 2016 • Thien Huu Nguyen, Nicolas Fauceglia, Mariano Rodriguez Muro, Oktie Hassanzadeh, Alfio Massimiliano Gliozzo, Mohammad Sadoghi
Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence.
no code implementations • 24 Feb 2016 • Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, Radu Florian
One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages.
no code implementations • 18 Nov 2015 • Thien Huu Nguyen, Ralph Grishman
The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text.
Ranked #1 on Relation Extraction on ACE 2005 (Cross Sentence metric)
no code implementations • WS 2015 • Thien Huu Nguyen, Ralph Grishman
Ranked #1 on Relation Extraction on ACE 2005 (Cross Sentence metric)