Lei Wang


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Graph-to-Tree Learning for Solving Math Word Problems
Jipeng Zhang | Lei Wang | Roy Ka-Wei Lee | Yi Bin | Yan Wang | Jie Shao | Ee-Peng Lim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs. We conduct extensive experiments on two available datasets. Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly. We also discuss case studies and empirically examine Graph2Tree’s effectiveness in translating the MWP text into solution expressions.

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基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)
Haishun Liu (刘海顺) | Lei Wang (王雷) | Yanguang Chen (陈彦光) | Shuchen Zhang (张书晨) | Yuanyuan Sun (孙媛媛) | Hongfei Lin (林鸿飞)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



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Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
Jierui Li | Lei Wang | Jipeng Zhang | Yan Wang | Bing Tian Dai | Dongxiang Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.9% on Math23K with 5-fold cross-validation and from 69.2% to 76.1% on MAWPS.


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A Neural Network Based Model for Loanword Identification in Uyghur
Chenggang Mi | Yating Yang | Lei Wang | Xi Zhou | Tonghai Jiang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Translating a Math Word Problem to a Expression Tree
Lei Wang | Yan Wang | Deng Cai | Dongxiang Zhang | Xiaojiang Liu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.

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Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings
Chenggang Mi | Yating Yang | Lei Wang | Xi Zhou | Tonghai Jiang
Proceedings of the 27th International Conference on Computational Linguistics

To enrich vocabulary of low resource settings, we proposed a novel method which identify loanwords in monolingual corpora. More specifically, we first use cross-lingual word embeddings as the core feature to generate semantically related candidates based on comparable corpora and a small bilingual lexicon; then, a log-linear model which combines several shallow features such as pronunciation similarity and hybrid language model features to predict the final results. In this paper, we use Uyghur as the receipt language and try to detect loanwords in four donor languages: Arabic, Chinese, Persian and Russian. We conduct two groups of experiments to evaluate the effectiveness of our proposed approach: loanword identification and OOV translation in four language pairs and eight translation directions (Uyghur-Arabic, Arabic-Uyghur, Uyghur-Chinese, Chinese-Uyghur, Uyghur-Persian, Persian-Uyghur, Uyghur-Russian, and Russian-Uyghur). Experimental results on loanword identification show that our method outperforms other baseline models significantly. Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0.5-0.9 BLEU improvements)


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Log-linear Models for Uyghur Segmentation in Spoken Language Translation
Chenggang Mi | Yating Yang | Rui Dong | Xi Zhou | Lei Wang | Xiao Li | Tonghai Jiang
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random field (CRF) feature, bilingual word alignment feature and monolingual suffixword co-occurrence feature. Experimental results shown that our proposed segmentation model for Uyghur spoken translation achieved 1.6 BLEU score improvements compared with the state-of-the-art baseline.


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Recurrent Neural Network Based Loanwords Identification in Uyghur
Chenggang Mi | Yating Yang | Xi Zhou | Lei Wang | Xiao Li | Tonghai Jiang
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers


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Construction of Chinese Idiom Knowledge-base and Its Applications
Lei Wang | Shiwen Yu
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications

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Semantic Computing and Language Knowledge Bases
Lei Wang | Shiwen Yu
CIPS-SIGHAN Joint Conference on Chinese Language Processing