Papers by Yongyi Mao
The APVA-TURBO Approach To Question Answering in Knowledge Base (C18-1)
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| Challenge: | Existing query languages for question answering over knowledge bases are not capable of processing queries presented in human language directly. |
| Approach: | They advocate a new model architecture that includes a verification mechanism for checking the correctness of predicted relations. |
| Outcome: | The proposed approach dramatically improves the question answering performance. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
DropMix: A Textual Data Augmentation Combining Dropout with Mixup (2022.emnlp-main)
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| Challenge: | Existing methods to overcome overfitting in text learning do not consider dimensionality . dimensionalization is important for deep neural networks to overcome the problem . |
| Approach: | They propose a saliency map-based approach to overcome overfitting in text learning . they propose augmentation regularization methods such as Dropout and Mixup to improve regularization . |
| Outcome: | Empirical results show that the proposed approach overcomes overfitting in text learning . dropout and mixup methods are effective in enhancing regularization . |
Learning VAE-LDA Models with Rounded Reparameterization Trick (2020.emnlp-main)
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| Challenge: | No reparameterization form of Dirichlet distributions is known to date for topic models . |
| Approach: | They propose a method to reparameterize Dirichlet distributions for the learning of VAE-LDA models by using a latent Dirichlets prior. |
| Outcome: | The proposed method outperforms existing neural topic models on benchmark datasets and on a synthetic dataset. |
Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)
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| Challenge: | Existing models for text style transfer suffer from two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure lack diversity and semantic consistency. |
| Approach: | They propose a style transfer model with adversarial masking and styled filling techniques to solve these challenges. |
| Outcome: | The proposed model performs well on two benchmark text style transfer data sets. |
Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding (2022.emnlp-main)
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| Challenge: | Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in an image using only caption-image annotations. |
| Approach: | They propose a novel contrastive learning framework that adaptively refines the target prediction by using only caption-image annotations. |
| Outcome: | The proposed framework outperforms existing methods on two widely used benchmarks, Flickr30K Entities and RefCOCO+. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking (2022.findings-acl)
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| Challenge: | Recent work on entity linking has focused on the zero-shot scenario where at test time the entity mention to be labelled is never seen during training. |
| Approach: | They propose a transformational biencoder that integrates a transform into BERT to perform a zero-shot transfer from the source domain to the target domain. |
| Outcome: | The proposed model performs a zero-shot transfer from the source domain to the target domain on a benchmark dataset and achieves new state-of-the-art. |
Anaphor Assisted Document-Level Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction are incomplete and lack anaphor for identifying relations between entities. |
| Approach: | They propose an Anaphor-Assisted (AA) framework for document-level relation extraction . they use a document or sentences as intermediate nodes to model cross-sentence entity interactions . |
| Outcome: | The proposed framework achieves state-of-the-art on the widely-used datasets. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
Hypernym Discovery via a Recurrent Mapping Model (2021.findings-acl)
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| Challenge: | Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model. |
| Approach: | They propose a parallel style model that maps query words to their hypernyms . they use a lexical-semantic relation to name a specific instance or subtype hyponym . |
| Outcome: | Empirical results on SemEval-2018 Task 9 confirm the effectiveness of the proposed model. |
Syntax Encoding with Application in Authorship Attribution (D18-1)
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| Challenge: | Existing approaches to extract syntactic features from text or sentences are limited by the loss of rich structural information contained in the syntax tree. |
| Approach: | They propose to embed the syntax parse tree of sentence into a learnable distributed representation . they show that the approach improves upon the prior art and achieves new performance records . |
| Outcome: | The proposed approach improves upon the prior art and achieves new performance records on five benchmarking data sets. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
| Outcome: | Empirical results show that the proposed model improves on two real-world datasets. |
Parameter-free Automatically Prompting: A Latent Pseudo Label Mapping Model for Prompt-based Learning (2022.findings-emnlp)
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| Challenge: | Existing manual label mapping methods that require extra parameters and human knowledge are limited in data. |
| Approach: | They propose a Latent Pseudo Label Mapping method that optimizes the label mapping without human knowledge and extra parameters. |
| Outcome: | The proposed method outperforms the standard SOTA method in few-shot learning tasks and significantly outperformed the standard ALM method which requires extra task-specific prior knowledge. |
Parallel Interactive Networks for Multi-Domain Dialogue State Generation (2020.emnlp-main)
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| Challenge: | Existing models do not account for the dependencies between system and user utterances in the same turn and across different turns. |
| Approach: | They propose to integrate an interactive encoder to jointly model in-turn dependencies and cross-turn dependents. |
| Outcome: | The proposed model is superior to existing models and can be used to selectively copy words from historical system utterances or historical user utterrances. |