Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation (2020)

Abstract: Point-of-Interest (POI) oriented question answering (QA) aims to return a list of POIs given a question issued by a user. Recent advances in intelligent virtual assistants have opened the possibility of engaging the client software more actively in the provision of location-based services, thereby showing great promise for automatic POI retrieval. Some existing QA methods can be adopted on this task such as QA similarity calculation and semantic parsing using pre-defined rules. The returned results, however, are subject to inherent limitations due to the lack of the ability for handling some important POI related information, including tags, location entities, and proximity-related terms (e.g. “nearby”, “close”). In this paper, we present a novel deep learning framework integrated with joint inference to capture both tag semantic and geographic correlation between question and POIs. One characteristic of our model is to propose a special cross attention question embedding neural network structure to obtain question-to-POI and POI-to-question information. Besides, we utilize a skewed distribution to simulate the spatial relationship between questions and POIs. By measuring the results offered by the model against existing methods, we demonstrate its robustness and practicability, and supplement our conclusions with empirical evidence.

## introduction point-of-interest (poi) oriented question answering (qa) problem is a special qa task which aims to answer users' questions by generating a list of pois. with the rapid development of smart agents (e.g. amazon echo) and intelligent virtual assistants (e.g. apple siri and google assistant), there are many poi oriented queries being requested everyday. some examples of these questions are "where can i take my kid to have fun nearby new york city" or "where can we go in la with my friends". the answers are typically a list of pois such as parks, malls, or restaurants corresponding to the details provided by users in the questions. according to some statistics, there are millions of poi oriented qa questions being requested per day on a mobile search engine in china. generally speaking, semantic parsing and similarity matching methods are utilized to tackle the poi oriented qa problem in current solutions. nevertheless, both of them are subject to inherent limitations and deserve to be improved. semantic parsing based methods convert the questions to formal representations (such as sql queries) using pre-defined rules, then get the poi results from the query. difficulties arise, however, when the form of the questions varies from person to person. besides, due to ambiguous expressions of a specific tag, semantic parsing methods always fail to match mentioned tags to the poi database. furthermore, based only on tag information, it is almost impossible for semantic parsing methods to make use of the distance correlation between questions and pois. another line of solution is adopting similarity matching models for calculating the similarity score between questions and pois. recent years have witnessed rapid growth in various kinds of semantic similarity based qa systems such as convolutional neural network architecture @xcite , lstm based answer selection @xcite , and cross-attention based question answering system @xcite . despite the success in common landscapes, most existing studies of this family cannot work well for poi oriented qa, since it is ineffective for them to handle the unique properties of poi elements such as tags and locations. as a result, a significant gap remains between academic proposals and the industry standard of implementing location based services. it is nontrivial to extend existing qa models to handle the challenges of poi oriented qa. in general, the unique challenges for this problem mainly come from two aspects. first of all, when asking poi oriented questions, people tend to emphasize certain needs, which correspond to some poi properties, such as the popular users of the poi, the service provided by the poi, and the types of the poi, etc. hereafter we name all such poi properties as tags. identifying such information in the question that is related to the tags of poi is crucial in this task, thus creating a bottleneck. take the question "where can children go nearby new york city" as an example, the word "children", being regarded as both a question term and a poi tag, plays an important role in identifying the corresponding pois. second, proximity-related terms such as "nearby" and "close" deserve special treatment. considering the same example, if there is "nearby" in the question, the candidate pois should be mainly located outside new york city; whereas if without, the candidate pois should be within new york city. furthermore, for different location entities such as "nearby new york city" v.s. "nearby manhattan", the distance scopes of "nearby" are also different. in contrast, traditional qa methods are not able to treat these terms in their models properly and thus leading to a poor performance on poi oriented qa. in this paper, we propose a poi oriented qa model with joint inference (named as pji for short) to tackle the challenges mentioned before. pji mainly has two modules which are named as tag semantic module and distance correlation module. the tag semantic module is used to automatically search for relevant pois based on semantic tag in-formation. besides, in order to capture specific patterns buried in questions and pois, we develop a novel cross attention based question embedding structure. therefore the mutual influence between questions and pois is taken into account. in the distance correlation module, we adopt a skewed distribution on three-level locations including city, district, area of interest (aoi) to fit the distance distribution between candidate pois and mentioned location terms in the question. both modules are fused together and optimized in an end-to-end manner for retrieving the final poi list. our major contributions can be summarized as follows: neural approaches for qa with the recent development in deep learning, neural networks have achieved great success in question answer problems @xcite @xcite @xcite . most of these models use a deep neural network like gru @xcite and lstm @xcite to handle the long texts required for qa. further improvements like attention mechanism are applied to focus on the most relevant facts @xcite . the relevance score of each qa pair is the cosine similarity of the semantic vectors. the final answers to each question are then sorted by the similarity score. ## conclusion in this paper, we propose a novel deep learning framework with joint inference to solve the poi oriented question answering task. our main contributions lie in three aspects. first, this model handles the poi oriented qa with the help of tag semantic module and distance correlation module. second, by introducing a cross attention based question embedding structure, we achieve a precise and flexible representation of questions. third, the proposed model can overcome several challenges of poi oriented qa including poi tag recognition, proximity-related term processing and diverse distance correlation. extensive experiments on two real-world datasets are carried out to demonstrate the effectiveness of our model. the result shows that our approach outperforms all the baselines and state-of-the-art models.