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Combating with extremely noisy samples in weakly supervised slot filling for automatic diagnosis

Frontiers of Computer Science 2023年第5期17卷 67-73页

作者： Xiaoming SHI Wanxiang CHE School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic *** responses can be regarded as the weak supervision of patient *** this way,a large amount of weakly labeled data can be obtained from unlabeled diagnosis dialogue,alleviating the problem of costly and time-consuming data ***,weakly labeled data suffers from extremely noisy *** alleviate the problem,we propose a simple and effective Co-WeakTeaching *** method trains two slot filling models *** two models learn from two different weakly labeled data,ensuring learning from two ***,one model utilizes selected weakly labeled data generated by the other,*** model,obtained by the Co-WeakTeaching on weakly labeled data,can be directly tested on testing data or sequentially fine-tuned on a small amount of human-annotated *** results on these two settings illustrate the effectiveness of the method with an increase of 8.03%and 14.74%in micro and macro f1 scores,respectively.

关键词： dialogue system slot filling co-teaching

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Intent-slot Correlation Modeling for Joint Intent Prediction and slot filling

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Journal of Computer Science & Technology 2022年第2期37卷 309-319页

作者：樊骏锋汪美玲李长亮朱自强毛璐 AI Laboratory KingSoft CorporationBeijing 100190China

slot filling and intent prediction are basic tasks in capturing semantic frame of human *** and intent have strong correlation for semantic frame *** each utterance,a specific intent type is generally determined with the indication information of words having slot tags(called as slot words),and in reverse the intent type decides that words of certain categories should be used to fill as ***,the Intent-slot correlation is rarely modeled explicitly in existing studies,and hence may be not fully *** this paper,we model Intent-slot correlation explicitly and propose a new framework for joint intent prediction and slot ***,we explore the effects of slot words on intent by differentiating them from the other words,and we recognize slot words by solving a sequence labeling task with the bi-directional long short-term memory(BiLSTM)***,slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic *** addition,we integrate the slot-Gated mechanism into slot filling to model dependency of slots on ***,we obtain slot recognition,intent prediction and slot filling by training with joint *** results on the benchmark Air-line Travel Information System(ATIS)and Snips datasets show that our Intent-slot correlation model achieves state-of-the-art semantic frame performance with a lightweight structure.

关键词： spoken language understanding slot filling intent prediction Intent-slot correlation slot recognition

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CN-DBpedia2: An Extraction and Verification Framework for Enriching Chinese Encyclopedia Knowledge Base

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Data Intelligence 2019年第3期1卷 271-288页

作者： Bo Xu Jiaqing Liang Chenhao Xie Bin Liang Lihan Chen Yanghua Xiao School of Computer Science and Technology Donghua UniversityShanghai 200051China Shanghai Key Laboratory of Data Science School of Computer ScienceFudan UniversityShanghai 200433China

Knowledge base plays an important role in machine understanding and has been widely used in various applications, such as search engine, recommendation system and question answering. However, most knowledge bases are incomplete, which can cause many downstream applications to perform poorly because they cannot find the corresponding facts in the knowledge bases. In this paper, we propose an extraction and verification framework to enrich the knowledge bases. Specifically, based on the existing knowledge base, we first extract new facts from the description texts of entities. But not all newly-formed facts can be added directly to the knowledge base because the errors might be involved by the extraction. Then we propose a novel crowd-sourcing based verification step to verify the candidate facts. Finally, we apply this framework to the existing knowledge base CN-DBpedia and construct a new version of knowledge base CN-DBpedia2, which additionally contains the high confidence facts extracted from the description texts of entities.

关键词： Knowledge graph Entity typing slot filling Information extraction Crowdsourcing

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MDKB-Bot:A Practical Framework for Multi-Domain Task-Oriented Dialogue System

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Data Intelligence 2019年第2期1卷 176-186页

作者： Yadi Lao Weijie Liu Sheng Gao Si Li Pattern Recognition and Intelligence System Laboratory Beijing University of Posts and TelecommunicationsBeijing 100876China

One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or ***,the encoder-decoder model based on the end-to-end neural network has become an attractive approach to meet this ***,it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state *** paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT(MDKB-BOT),which leverages both neural networks and rule-based strategy in natural language understanding(NLU)and dialogue management(DM).Experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign show that MDKB-BOT achieves competitive performance on several evaluation metrics,including task completion rate and user satisfaction.

关键词： Dialogue system Knowledge base Natural language understanding slot filling Natural language generation

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Research on Intent-slot Recognition Algorithm Based on Knowledge Graph and User Topic

Research on Intent-Slot Recognition Algorithm Based on Knowl...

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第40届中国控制会议

作者： Nan Hao Pengwei Xie Bo Chen School of Automation Beijing Institute of Technology

In this article, we propose a joint recognition method of intention and slot. The model can directly obtain the user’s intention and slot information through the user’s historical interaction information, knowledge graph and current input. In this method, we first use a knowledge reasoning module based on user topic and knowledge graph. The topic model is used to filter the user’s historical information, remove meaningless chat information, retain the most important topic information, and further obtain the external information vector from the knowledge graph which is helpful for current intention recognition and slot *** addition, we use attention mechanism modules to fuse the historical information, external knowledge vector and sentence features, and suppress the invalid part of external information. Experiments on the data sets show that our method can effectively improve the accuracy of intention recognition and F value of slot filling.

关键词： Intent recognition Knowledge graph User topic slot filling

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