Answering Product-related Questions with Heterogeneous Information (2020)

Abstract: Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users’ online shopping experience. However, existing product question answering (PQA) methods only consider a single information source such as user reviews and/or require large amounts of labeled data. In this paper, we propose a novel framework to tackle the PQA task via exploiting heterogeneous information including natural language text and attribute-value pairs from two information sources of the concerned product, namely product details and user reviews. A heterogeneous information encoding component is then designed for obtaining unified representations of information with different formats. The sources of the candidate snippets are also incorporated when measuring the question-snippet relevance. Moreover, the framework is trained with a specifically designed weak supervision paradigm making use of available answers in the training phase. Experiments on a real-world dataset show that our proposed framework achieves superior performance over state-of-the-art models.

## introduction to help potential consumers address their concerns during online shopping, many e-commerce sites now provide a community question answering (cqa) platform, where users can post questions for a specific product, and others can voluntarily answer them. very often, it takes a long time for an asker to wait for an answer on such platforms. therefore, automatically providing a proper response to a product-related question can greatly improve user online shopping experience and stimulate purchase decisions. several efforts have been made to tackle such product-related question answering (pqa) task @xcite @xcite @xcite . the existing methods can be generally categorized regarding the involved information source, i.e., from where the responses are obtained. a pioneer work by @xcite investigates answer selection via detecting clues from user reviews. from then on, the review set becomes a commonly used auxiliary information for predicting the answer types or distinguishing true answers from randomly sampled ones @xcite . however, these methods are not feasible for newly-posted questions without candidate answers. a recent approach for pqa task is to directly extract review sentences as the response for a given question @xcite . but it requires a large number of labeled question-review pairs, whose annotation is a time-consuming and laborious work. other information sources, such as existing qa collections, are also exploited @xcite , but relevant qa pairs are assumed to be always available for a new question in their setting, which is uncommon in practice. besides user reviews, another kind of information, namely product details provided by the manufacturer are always available and can be an important information source for addressing productrelated questions. for example, considering the question "how large is the keyboard" for the product shown in figure 1 , the attribute-value pair "item dimensions: 10.9×4.8×0.6 in" from the specification table can be a good response. such information can be essential for questions looking for factual type information due to their reliability and preciseness, but they are often underutilized in previous works. the above scenario motivates our task of answering product-related questions via exploiting the information from both product details and user reviews to obtain relevant snippets serving as responses for improving user satisfaction. this task presents some new research challenges: (i) the heterogeneity of candidate information needs to be appropriately handled. from the above example, we can see that there exists both attributevalue pairs and natural language texts as candidate responses, which implies that typical answer selection approaches @xcite @xcite are incapable of handling the concerned task. (ii) product details and user reviews contain different types of information, which are suitable for answering questions with different information needs. returning to the example in figure 1 , considering a more subjective question asking about user experience "how is the key travel", snippets from reviews such as "...good key travel and solid feel.." can provide more appropriate responses. thus, we can observe that questions with different intents can be better answered by snippets from different sources, which should be exploited when measuring the question-snippet relevance. (iii) training a model to capture the relevance between a question and a candidate snippet with typical supervised paradigms requires a large volume of labeled data. however, it is very timeconsuming to manually label the question-snippet pairs in the pqa task due to the product-specific nature of questions and candidate snippets @xcite , which demands a better solution for training such models. to tackle these challenges, we propose a novel framework for the pqa task using heterogenous information via a weak supervision paradigm (hiws). given a product-related question, hiws exploits the corresponding product details and user reviews to return a ranked snippet list serving as the response. specifically, a heterogeneous infor-mation encoding component is first developed to encode different information formats into a unified representation composed of a free text sentence and a set of focused aspects. then for measuring the question-snippet relevance, a gated fusion approach is designed to get aspect-enhanced representations. also, a question intent analysis module is designed to better determine which information source is more suitable for providing responses. to handle the shortage of labeled data for model training, we develop a weak supervision paradigm making use of the original user-posted answers during training. some external resources including pre-trained language models such as bert @xcite are utilized to obtain weak supervision signals to facilitate the training process. our main contributions are as follows: ## the proposed framework for a product p, its associated information can be represented as a tuple c @xmath0, d, r), where @xmath1, v i )} is a set of attribute-value pairs extracted from the corresponding specification table. @xmath2, @xmath3. now given a question q regarding the product p, our task is to automatically rank the candidate snippets in c p , which can either be a textual sentence from d or r, or an attribute-value pair from a for providing responses to the question q. as shown in figure 2 , hiws mainly consists of three components: heterogeneous information encoding, question-snippet relevance matching, and automatic label construction. concretely, the candidate snippets are first transformed into unified representations. then we measure the questionsnippet relevance both from their aspect-enhanced representations and the intent matching. the overall model is then trained using the automaticallyconstructed labels via making use of the original answer to the given question. ## related work in recent years, many deep learning based methods have been proposed for the answer selection task in community question answering (cqa) platforms. these models can be generally categorized into two types according to their network architecture @xcite , namely siamese networks @xcite and compare-aggregate networks (wang product-related question answering (pqa) problem has drawn a lot of attention recently, due to the increasing popularity of online shopping. most of the existing works utilize reviews as their major information to provide responses for a given question. mcauley and yang (2016) treat reviews as "experts" to handle the answer selection task. later, product aspects are considered to further improve the performance @xcite . @xcite propose to tackle pqa task by directly retrieving review sentences as answers. however, it requires a large number of labeled questionreview pairs. @xcite assume that relevant qa pairs are always available for a given question which can be utilized to provide the responses. some other works formulate the pqa task as a reading comprehension problem @xcite , where the main focus is to extract a text span as the answer given a relevant review, which is unavailable in many cases. given some successful applications of text generation models such as text summarization @xcite and response generation @xcite , some models are proposed to generate an answer sentence @xcite given relevant product information, some later works specifically consider the user opinion information during such generation process @xcite . since most product-related questions are looking for diverse answers, we argue that information extracted from reliable sources is more effective and explainable solution for the pqa task. more recently, some studies consider the answer helpfulness prediction task @xcite and answer ranking problem @xcite in the context of pqa, assuming the existence of user-provided answers to a given question. different from them, we aim to provide instant responses for a newly-posted question in e-commerce. ## conclusions we propose a novel framework for answering product-related questions via exploiting heterogeneous information including attribute-value pairs and free text sentences from both product details and user reviews. to tackle the shortage of labeled data, we design a weak supervision paradigm by making use of the existing qa pairs to automatically construct labels for training. extensive experiments conducted on a real-word dataset demonstrate the superiority of our proposed framework.