Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020)

Abstract: Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants. In this paper, we first study the LU problem in the spatial domain, which is a critical problem for providing location-based services by voice assistants but is without in-depth investigation in existing studies. Spatial domain queries have several unique properties making them be more challenging for language understanding than common conversational queries, including lexical-similar but diverse intents and highly ambiguous words. Thus, a special tailored LU framework for spatial domain queries is necessary. To the end, a dataset was extracted and annotated based on the real-life queries from a voice assistant service. We then proposed a new multi-task framework that jointly learns the intent detection and entity linking tasks on the with invented hierarchical intent detection method and triple-scoring mechanism for entity linking. A specially designed spatial GCN is also utilized to model spatial context information among entities. We have conducted extensive experimental evaluations with state-of-the-art entity linking and intent detection methods, which demonstrated that can outperform all baselines with a significant margin.

## introduction the past few years have witnessed the successful deployment of voice assistants on smart speakers (e.g. amazon echo) and mobile devices (e.g. apple siri and google assistant). as a critical step to facilitate informative responses by voice assistants, language understanding (lu) has attracted tremendous research attention in recent years @xcite @xcite . lu typically includes the intent detection which detects the categorical intent label, and the slot filling which indicates the slot type mentioned by certain words @xcite . in this paper, we first investigate the lu problem in the spatial domain. with the continuous improvement of their intelligence, virtual assistants are designed to provide many location-based services such as recommending restaurants @xcite and providing route planning @xcite . we name all such queries as spatial domain queries that usually contain some spatial information. similar to the lu of common conversational queries, there are also two main tasks for the lu of spatial domain queries. the first part is intent detection which aims to classify a user query into a scenario for further processing. an example of such intent can be "asking for the location information of poi". (here poi refers to point of interest, which is a place on a map like a restaurant or a shop.) building lu system for spatial domain queries has several unique challenges that have not been studied in-depth in previous works. at first, spatial query type mention type query intent ask for the distance information between two place around, aoi ask for the distant from around to aoi ask for the time information between two place city, province ask for the time from city to province ask for the location information of one place poi ask for the location information of poi ask for the information of one place except location poi ask for evaluation of poi ask for a recommendation tag ask for recommendations for attractions only one entity poi ask for the information of poi second, the entity linking in spatial domain queries is also a challenging problem. the mentions in spatial domain queries are quite diverse and ambiguous. for example, "juqi" is a common dialect word in beijing, but it also refers to a famous restaurant brand; "braised chicken rice" is a popular food in china, but it is also the name of many bistros. moreover, the candidate entities even share the same surface names. for example, there are two "xinhua garden" in beijing and many ones in china. how to correctly distinguish and link the entities is a challenging problem. to tackle the above challenges, in this paper, we propose a novel model melip tailored for language understanding of spatial domain queries, with working on a human-labeled real-life spatial domain query dataset smq (short for spatial domain queries). the core of melip is a multi-task learning framework that jointly learns the main tasks of the intent detection and entity linking. to overcome the lexical-similar but diverse intent challenge, we propose a hierarchical intent detection method with two auxiliary tasks which are query type prediction and mention type prediction. the query type task classifies each query into seven types as shown in smq is a real-life spatial domain query dataset collected from dueros foot_0 , one of the largest voice assistant services in china. smq has 55,000 pieces of spatial domain queries with human-labeled ground truth. we have conducted extensive experiment evaluation with the state-of-the-art query intent detection and entity linking methods on smq, and the results show that melip can significantly better handle these two tasks. we summarize our contributions as follows: ## model architecture we illustrate the proposed multi-task framework melip in figure 3 , consisting of a query intent detection module and an entity linking module in the poi-kb. query intent detection module is supported by two auxiliary tasks: query type prediction task and mention type prediction task. mention type prediction task also supports entity linking task. to sum up, melip is a multi-task framework with four tasks: two main tasks and two auxiliary tasks. hereafter, we use superscript of qt to denote query type, m to mentions, mt to mention type, int to query intent and el to entity linking. we record l int , l el , l qt , l mt as the loss functions for query intent detection task, entity linking task, query type prediction task and mention type prediction task respectively. the final loss function is defined as follows. we jointly trained melip through the above loss function. next, we will describe the two main tasks and how the two auxiliary tasks support them. ## conclusion in this paper, we study the language understanding problem on real-life spatial domain queries. we proposed a hierarchical intent detection method to overcome the lexical-similar but diverse intent challenge. we also designed a triple-scoring solution to entity linking from the diverse and ambiguous query words. considering the interaction between query intent detection and entity linking, a multi-task framework melip is designed for jointly learning the two main tasks and two auxiliary tasks. the performance of melip on a large scale dataset smq is significantly better than the state-of-the-art models. we jointly trained the two main task and two auxiliary task with the loss function eqn. 8. λ 1 was 1 and λ 2 was 0.6. the hidden state size d is 300 for all cnn and rcnn modules. the chinese word embedding modules were all initialed with word2vec @xcite . the gcn layer number in the sgcn pre-training model was 2. the n qi was 100. the learning rate was set as 0.001 for rcnn, 0.0001 for cnn in query intent detection module and 0.01 for entity linking module. all the parameters were optimized with adam optimizer(kingma and ba, 2014) and the batch size was 16. we trained the model with 20 epochs and an early stop mechanism was used when the accuracy on the validation set did not increase over ten batches. the hyper-parameters were evaluated on validation results.