Jinghui Xiao

Also published as: JingHui Xiao


2022

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Program Transfer for Answering Complex Questions over Knowledge Bases
Shulin Cao | Jiaxin Shi | Zijun Yao | Xin Lv | Jifan Yu | Lei Hou | Juanzi Li | Zhiyuan Liu | Jinghui Xiao
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer. Learning to induce programs relies on a large number of parallel question-program pairs for the given KB. However, for most KBs, the gold program annotations are usually lacking, making learning difficult. In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations. For program transfer, we design a novel two-stage parsing framework with an efficient ontology-guided pruning strategy. First, a sketch parser translates the question into a high-level program sketch, which is the composition of functions. Second, given the question and sketch, an argument parser searches the detailed arguments from the KB for functions. During the searching, we incorporate the KB ontology to prune the search space. The experiments on ComplexWebQuestions and WebQuestionSP show that our method outperforms SOTA methods significantly, demonstrating the effectiveness of program transfer and our framework. Our codes and datasets can be obtained from https://github.com/THU-KEG/ProgramTransfer.

2020

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HyperText: Endowing FastText with Hyperbolic Geometry
Yudong Zhu | Di Zhou | Jinghui Xiao | Xin Jiang | Xiao Chen | Qun Liu
Findings of the Association for Computational Linguistics: EMNLP 2020

Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym hierarchy in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not represent such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modelling tree-like hierarchical data, we propose a new model named HyperText for efficient text classification by endowing FastText with hyperbolic geometry. Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters.

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BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Bin He | Di Zhou | Jinghui Xiao | Xin Jiang | Qun Liu | Nicholas Jing Yuan | Tong Xu
Findings of the Association for Computational Linguistics: EMNLP 2020

Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG. Then, the learned knowledge is integrated with a pre-trained language model to do the knowledge generalization. Experimental results demonstrate that our model achieves the state-of-the-art performance on several medical NLP tasks, and the improvement above MedERNIE indicates that graph contextualized knowledge is beneficial.

2007

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An Empirical Study of Non-Stationary Ngram Model and its Smoothing Techniques
Jinghui Xiao | Bingquan Liu | Xiaolong Wang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 2, June 2007

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Exploiting Pinyin Constraints in Pinyin-to-Character Conversion Task: a Class-Based Maximum Entropy Markov Model Approach
Jinghui Xiao | Bingquan Liu | Xiaolong Wang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 3, September 2007: Special Issue on Invited Papers from ISCSLP 2006

2005

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Principles of Non-stationary Hidden Markov Model and Its Applications to Sequence Labeling Task
JingHui Xiao | BingQuan Liu | XiaoLong Wang
Second International Joint Conference on Natural Language Processing: Full Papers