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

Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic diagnosis.Doctor responses can be regarded as the weak supervision of patient queries.In 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 annotation.However,weakly labeled data suffers from extremely noisy samples.To alleviate the problem,we propose a simple and effective Co-WeakTeaching method.The method trains two slot filling models simultaneously.These two models learn from two different weakly labeled data,ensuring learning from two aspects.Then,one model utilizes selected weakly labeled data generated by the other,iteratively.The 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 data.Experimental 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.

Intent-Slot Correlation Modeling for Joint Intent Prediction and Slot Filling

Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances.Slots and intent have strong correlation for semantic frame parsing.For 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 slots.However,the Intent-Slot correlation is rarely modeled explicitly in existing studies,and hence may be not fully exploited.In this paper,we model Intent-Slot correlation explicitly and propose a new framework for joint intent prediction and slot filling.Firstly,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)model.Then,slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic results.In addition,we integrate the Slot-Gated mechanism into slot filling to model dependency of slots on intent.Finally,we obtain slot recognition,intent prediction and slot filling by training with joint optimization.Experimental 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.

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

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.

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

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 restaurants.Recently,the encoder-decoder model based on the end-to-end neural network has become an attractive approach to meet this challenge.However,it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition.This 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.