Ruoyu Zhang


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Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base
Minhao Zhang | Ruoyu Zhang | Yanzeng Li | Lei Zou
Findings of the Association for Computational Linguistics: NAACL 2022

Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect and connect related nodes in a query. Despite the strong causal effects between NE and GC, previous works fail to directly model such causalities in their pipeline, hindering the learning of subtask correlations. Also, the sequence-generation process for GC in previous works induces ambiguity and exposure bias, which further harms accuracy. In this work, we formalize semantic parsing into two stages. In the first stage (graph structure generation), we propose a causal-enhanced table-filler to overcome the issues in sequence-modelling and to learn the internal causalities. In the second stage (relation extraction), an efficient beam-search algorithm is presented to scale complex queries on large-scale KBs. Experiments on LC-QuAD 1.0 indicate that our method surpasses previous state-of-the-arts by a large margin (17%) while remaining time and space efficiency.


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NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering
Minhao Zhang | Ruoyu Zhang | Lei Zou | Yinnian Lin | Sen Hu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. Equipped with techniques including data augmentation and multitasking, we show that the proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. Moreover, we develop a novel data annotation strategy that facilitates the node-to-mention alignment, a dataset ( with such strategy is also published to promote further research. An online demo of NAMER ( is provided to visualize our framework and supply extra information for users, a video illustration ( of NAMER is also available.


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MedDialog: Large-scale Medical Dialogue Datasets
Guangtao Zeng | Wenmian Yang | Zeqian Ju | Yue Yang | Sicheng Wang | Ruisi Zhang | Meng Zhou | Jiaqi Zeng | Xiangyu Dong | Ruoyu Zhang | Hongchao Fang | Penghui Zhu | Shu Chen | Pengtao Xie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets – MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases. To our best knowledge, MedDialog is the largest medical dialogue dataset to date. We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance. It is shown that models trained on MedDialog are able to generate clinically correct and doctor-like medical dialogues. We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks. It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation. The datasets and code are available at