Binhua Li


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SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
Bowen Qin | Lihan Wang | Binyuan Hui | Bowen Li | Xiangpeng Wei | Binhua Li | Fei Huang | Luo Si | Min Yang | Yongbin Li
Proceedings of the 29th International Conference on Computational Linguistics

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions. Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results.

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Graph-to-Text Generation with Dynamic Structure Pruning
Liang Li | Ruiying Geng | Bowen Li | Can Ma | Yinliang Yue | Binhua Li | Yongbin Li
Proceedings of the 29th International Conference on Computational Linguistics

Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism. Recent studies have shown that explicitly modeling the input graph structure can significantly improve the performance. However, the vanilla structural encoder cannot capture all specialized information in a single forward pass for all decoding steps, resulting in inaccurate semantic representations. Meanwhile, the input graph is flatted as an unordered sequence in the cross attention, ignoring the original graph structure. As a result, the obtained input graph context vector in the decoder may be flawed. To address these issues, we propose a Structure-Aware Cross-Attention (SACA) mechanism to re-encode the input graph representation conditioning on the newly generated context at each decoding step in a structure aware manner. We further adapt SACA and introduce its variant Dynamic Graph Pruning (DGP) mechanism to dynamically drop irrelevant nodes in the decoding process. We achieve new state-of-the-art results on two graph-to-text datasets, LDC2020T02 and ENT-DESC, with only minor increase on computational cost.


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Dynamic Memory Induction Networks for Few-Shot Text Classification
Ruiying Geng | Binhua Li | Yongbin Li | Jian Sun | Xiaodan Zhu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper proposes Dynamic Memory Induction Networks (DMIN) for few-short text classification. The model develops a dynamic routing mechanism over static memory, enabling it to better adapt to unseen classes, a critical capability for few-short classification. The model also expands the induction process with supervised learning weights and query information to enhance the generalization ability of meta-learning. The proposed model brings forward the state-of-the-art performance significantly by 2~4% improvement on the miniRCV1 and ODIC datasets. Detailed analysis is further performed to show how the proposed network achieves the new performance.


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Induction Networks for Few-Shot Text Classification
Ruiying Geng | Binhua Li | Yongbin Li | Xiaodan Zhu | Ping Jian | Jian Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.