Mairgup Mansur

Also published as: Mansur Mairgup


DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
Tianyang Cao | Shuang Zeng | Xiaodan Xu | Mairgup Mansur | Baobao Chang
Proceedings of the 29th International Conference on Computational Linguistics

A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations. Successful MWP generation can automate the writing of mathematics questions. Previous methods mainly generate MWP text based on inflexible pre-defined templates. In this paper, we propose a neural model for generating MWP text from math equations. Firstly, we incorporate a matching model conditioned on the domain knowledge to retrieve a MWP instance which is most consistent with the ground-truth, where the domain is a latent variable extracted with a domain summarizer. Secondly, by constructing a Quantity Cell Graph (QCG) from the retrieved MWP instance and reasoning over it, we improve the model’s comprehension of real-world scenarios and derive a domain-constrained instance sketch to guide the generation. Besides, the QCG also interacts with the equation encoder to enhance the alignment between math tokens (e.g., quantities and variables) and MWP text. Experiments and empirical analysis on educational MWP set show that our model achieves impressive performance in both automatic evaluation metrics and human evaluation metrics.


Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data
Wenhui Wang | Baobao Chang | Mairgup Mansur
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35% UAS and 95.25% LAS.


Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation
Longkai Zhang | Houfeng Wang | Xu Sun | Mairgup Mansur
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Feature-based Neural Language Model and Chinese Word Segmentation
Mairgup Mansur | Wenzhe Pei | Baobao Chang
Proceedings of the Sixth International Joint Conference on Natural Language Processing


Chinese word segmentation model using bootstrapping
Baobao Chang | Mansur Mairgup
CIPS-SIGHAN Joint Conference on Chinese Language Processing