Quan Hung Tran

Also published as: Quan Tran


2021

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A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
Tuan Lai | Heng Ji | Trung Bui | Quan Hung Tran | Franck Dernoncourt | Walter Chang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pre-trained language models, we argue that it is still highly beneficial to utilize symbolic features for the task. However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain errors. Also, depending on the specific context, some features can be more informative than others. Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features. Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016.

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Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
Tuan Lai | Heng Ji | ChengXiang Zhai | Quan Hung Tran
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-the-art results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity and relation extraction tasks

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TIMERS: Document-level Temporal Relation Extraction
Puneet Mathur | Rajiv Jain | Franck Dernoncourt | Vlad Morariu | Quan Hung Tran | Dinesh Manocha
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.

2020

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A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents
Tuan Lai | Trung Bui | Doo Soon Kim | Quan Hung Tran
Proceedings of the 28th International Conference on Computational Linguistics

Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.

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What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation
Amir Pouran Ben Veyseh | Franck Dernoncourt | Quan Hung Tran | Thien Huu Nguyen
Proceedings of the 28th International Conference on Computational Linguistics

Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in documents and serve as one of the mainstays of writing. Due to their importance, identifying acronyms and corresponding phrases (i.e., acronym identification (AI)) and finding the correct meaning of each acronym (i.e., acronym disambiguation (AD)) are crucial for text understanding. Despite the recent progress on this task, there are some limitations in the existing datasets which hinder further improvement. More specifically, limited size of manually annotated AI datasets or noises in the automatically created acronym identification datasets obstruct designing advanced high-performing acronym identification models. Moreover, the existing datasets are mostly limited to the medical domain and ignore other domains. In order to address these two limitations, we first create a manually annotated large AI dataset for scientific domain. This dataset contains 17,506 sentences which is substantially larger than previous scientific AI datasets. Next, we prepare an AD dataset for scientific domain with 62,441 samples which is significantly larger than previous scientific AD dataset. Our experiments show that the existing state-of-the-art models fall far behind human-level performance on both datasets proposed by this work. In addition, we propose a new deep learning model which utilizes the syntactical structure of the sentence to expand an ambiguous acronym in a sentence. 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.

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Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering
Quan Hung Tran | Nhan Dam | Tuan Lai | Franck Dernoncourt | Trung Le | Nham Le | Dinh Phung
Proceedings of the 28th International Conference on Computational Linguistics

Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. Many previous works focused on visualizing internal components of neural networks to represent them through human-friendly concepts. On the other hand, in real life, when making a decision, human tends to rely on similar situations in the past. Thus, we argue that one potential approach to make the model interpretable and explainable is to design it in a way such that the model explicitly connects the current sample with the seen samples, and bases its decision on these samples. In this work, we design one such model: an explainable, evidence-based memory network architecture, which learns to summarize the dataset and extract supporting evidences to make its decision. The model achieves state-of-the-art performance on two popular question answering datasets, the TrecQA dataset and the WikiQA dataset. Via further analysis, we showed that this model can reliably trace the errors it has made in the validation step to the training instances that might have caused this error. We believe that this error-tracing capability might be beneficial in improving dataset quality in many applications.

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Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Khalil Mrini | Franck Dernoncourt | Quan Hung Tran | Trung Bui | Walter Chang | Ndapa Nakashole
Findings of the Association for Computational Linguistics: EMNLP 2020

Attention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used, however interpretability is difficult due to the numerous attention distributions. Recent work has shown that model representations can benefit from label-specific information, while facilitating interpretation of predictions. We introduce the Label Attention Layer: a new form of self-attention where attention heads represent labels. We test our novel layer by running constituency and dependency parsing experiments and show our new model obtains new state-of-the-art results for both tasks on both the Penn Treebank (PTB) and Chinese Treebank. Additionally, our model requires fewer self-attention layers compared to existing work. Finally, we find that the Label Attention heads learn relations between syntactic categories and show pathways to analyze errors.

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Scene Graph Modification Based on Natural Language Commands
Xuanli He | Quan Hung Tran | Gholamreza Haffari | Walter Chang | Zhe Lin | Trung Bui | Franck Dernoncourt | Nhan Dam
Findings of the Association for Computational Linguistics: EMNLP 2020

Structured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user’s command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.

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Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation
Amir Pouran Ben Veyseh | Nasim Nouri | Franck Dernoncourt | Quan Hung Tran | Dejing Dou | Thien Huu Nguyen
Findings of the Association for Computational Linguistics: EMNLP 2020

Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA. In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA. In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms. 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. The proposed model achieves the state-of-the-art performance on three benchmark datasets.

2019

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A Gated Self-attention Memory Network for Answer Selection
Tuan Lai | Quan Hung Tran | Trung Bui | Daisuke Kihara
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Answer selection is an important research problem, with applications in many areas. Previous deep learning based approaches for the task mainly adopt the Compare-Aggregate architecture that performs word-level comparison followed by aggregation. In this work, we take a departure from the popular Compare-Aggregate architecture, and instead, propose a new gated self-attention memory network for the task. Combined with a simple transfer learning technique from a large-scale online corpus, our model outperforms previous methods by a large margin, achieving new state-of-the-art results on two standard answer selection datasets: TrecQA and WikiQA.

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A Pointer Network Architecture for Context-Dependent Semantic Parsing
Xuanli He | Quan Tran | Gholamreza Haffari
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

2018

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The Context-Dependent Additive Recurrent Neural Net
Quan Hung Tran | Tuan Lai | Gholamreza Haffari | Ingrid Zukerman | Trung Bui | Hung Bui
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). Here, instead of relying solely on the information presented in the text, the learning agents have access to a strong external signal given to assist the learning process. In this paper, we propose a novel family of Recurrent Neural Network unit: the Context-dependent Additive Recurrent Neural Network (CARNN) that is designed specifically to address this type of problem. The experimental results on public datasets in the dialog problem (Babi dialog Task 6 and Frame), contextual language model (Switchboard and Penn Tree Bank) and question answering (Trec QA) show that our novel CARNN-based architectures outperform previous methods.

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Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
Xuanli He | Quan Tran | William Havard | Laurent Besacier | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the Australasian Language Technology Association Workshop 2018

In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)’s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.

2017

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A Generative Attentional Neural Network Model for Dialogue Act Classification
Quan Hung Tran | Gholamreza Haffari | Ingrid Zukerman
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a novel attentional technique and a label to label connection for sequence learning, akin to Hidden Markov Models. The experiments show that both of these innovations lead our model to outperform strong baselines for dialogue act classification on MapTask and Switchboard corpora. We further empirically analyse the effectiveness of each of the new innovations.

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Preserving Distributional Information in Dialogue Act Classification
Quan Hung Tran | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper introduces a novel training/decoding strategy for sequence labeling. Instead of greedily choosing a label at each time step, and using it for the next prediction, we retain the probability distribution over the current label, and pass this distribution to the next prediction. This approach allows us to avoid the effect of label bias and error propagation in sequence learning/decoding. Our experiments on dialogue act classification demonstrate the effectiveness of this approach. Even though our underlying neural network model is relatively simple, it outperforms more complex neural models, achieving state-of-the-art results on the MapTask and Switchboard corpora.

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Named Entity Recognition with Stack Residual LSTM and Trainable Bias Decoding
Quan Tran | Andrew MacKinlay | Antonio Jimeno Yepes
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.

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A Hierarchical Neural Model for Learning Sequences of Dialogue Acts
Quan Hung Tran | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.

2016

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Inter-document Contextual Language model
Quan Hung Tran | Ingrid Zukerman | Gholamreza Haffari
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity
Tu Thanh Vu | Quan Hung Tran | Son Bao Pham
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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JAIST: Combining multiple features for Answer Selection in Community Question Answering
Quan Hung Tran | Vu Duc Tran | Tu Thanh Vu | Minh Le Nguyen | Son Bao Pham
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)