Papers by Dongxiang Zhang
Translating a Math Word Problem to a Expression Tree (D18-1)
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| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification (2021.findings-acl)
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| Challenge: | Existing approaches for few-shot text classification rely on exploitation of lexical features and distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. |
| Approach: | They propose a meta-learning framework integrated with an adversarial domain adaptation network to improve the model's adaptive ability and generate high-quality text embedding for new classes. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on four datasets and shows clear superiority over existing models. |
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling (2022.coling-1)
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| Challenge: | Existing approaches to multiple intent detection and slot filling focus on task-specific components to capture the relationships between intents and slots. |
| Approach: | They propose a Unified Generative framework that captures the relationships between intents and slots in an utterance and formulates the task as a question-answering problem. |
| Outcome: | The proposed framework surpasses baselines on full-data and multi-intent benchmarks on 5-shot and 10-shot scenarios. |
TLM: Token-Level Masking for Transformers (2023.emnlp-main)
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| Challenge: | Structured dropout approaches have been investigated to regularize the multi-head attention mechanism in Transformers. |
| Approach: | They propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting by manipulating the connections between tokens in the multi-head attention via masking. |
| Outcome: | The proposed regularization scheme outperforms attention dropout and DropHead on 18 datasets and can establish a new record on the data-to-text benchmark Rotowire (18.93 BLEU). |
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (P19-1)
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| Challenge: | Several deep learning models have been proposed for solving math word problems (MWPs) but their approaches to capturing features are not specifically designed for MWP. |
| Approach: | They propose to use a group attention mechanism to extract global features, quantity-related features, quantities-pair features and question-related feature in MWPs. |
| Outcome: | The proposed approach performs significantly better than previous state-of-the-art methods and boosts performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.99% on Math 23K with 5-fold cross-validation and from 69.99% to 76.1% on MAWPS. |