Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2020)

Abstract: While online reviews of products and services become an important information source, it remains inefficient for potential consumers to exploit verbose reviews for fulfilling their information need. We propose to explore question generation as a new way of review information exploitation, namely generating questions that can be answered by the corresponding review sentences. One major challenge of this generation task is the lack of training data, i.e. explicit mapping relation between the user-posed questions and review sentences. To obtain proper training instances for the generation model, we propose an iterative learning framework with adaptive instance transfer and augmentation. To generate to the point questions about the major aspects in reviews, related features extracted in an unsupervised manner are incorporated without the burden of aspect annotation. Experiments on data from various categories of a popular E-commerce site demonstrate the effectiveness of the framework, as well as the potentials of the proposed review-based question generation task.

## introduction the user-written reviews for products or service have become an important information source and there are a few research areas analyzing such data, including aspect extraction @xcite , product recommendation @xcite , and sentiment analysis @xcite . reviews reflect certain concerns or experiences of users on products or services, and such information is valuable for other potential consumers. however, there are few mechanisms assisting users for efficient review digestion. it is time-consuming for users to locate critical review parts that they care about, particularly in long reviews. we propose to utilize question generation (qg) @xcite as a new means to overcome this problem. specifically, given a review sentence, the generated question is expected to ask about the concerned aspect of this product, from the perspective of the review writer. such question can be regarded as a reading anchor of the review sentence, and it is easier to view and conceive due to its concise form. as an example, the review for a battery case product in two unique characteristics of our review-based question generation task differentiate it from the previous question generation works. first, there is no review-question pairs available for training, thus a simple seq2seq-based question generation model for learning the mapping from the input (i.e. review) to the output (i.e. question) cannot be applied. even though we can easily obtain large volumes of user-posed review sets and question sets, they are just separate datasets and cannot provide any supervision of input-output mapping (i.e. reviewquestion pair). the second one is that different from the traditional question generation, the generated question from a review sentence will not simply take a fixed text segment in the review as its review question it doesn't heat up like most of the other ones, and i was completely fascinated by the ultra light and sleek design for the case. before i was using the mophie case but i couldn't wear it often because it was like having a hot brick in your pocket, hence i had to always leave it at home. on the contrary, with powerbear, i never take it off because i can't even tell the difference. also it is build in a super strong manner and even though i dropped my phone a few times, its shock resistant technology won't let a single thing happen to the case or the phone. the powerbear case became an extension to my phone that i never have to take off because when i charge it at night, it charges both my phone and the case. i have battery life for more than two days for normal use, i.e. not power-consuming gaming. does this make the phone warm during charging? have any of you that own this had a mophie? does this give protection to the phone? can this charge the phone and the extra battery at the same time? how many days it can last? the above two unique characteristics raise two challenges for our task. the first challenge, namely lacking review-question pairs as training instances, appears to be intractable, particularly given that the current end-to-end models are very data-hungry. one instant idea is to utilize user-posed (question, answer) pairs as substitute for training. however, several instance-related defects hinder the learned generation model from being competent for the review-based question generation. some answers are very short, e.g. "more than two days", therefore, without necessary context, they are not helpful to generate good questions. the second challenge, namely the issue that some verbose answers contain irrelevant content especially for subjective questions. to handle this challenge, we propose a learning framework with adaptive instance transfer and augmentation. firstly, a pre-trained generation model based on user-posed answer-question pairs is utilized as an initial question generator. a ranker is designed to work together with the generator to improve the training instance set by distilling it via removing unsuitable answer-question pairs to avoid "negative transfer" @xcite , and augmenting @xcite it by adding suitable reviewquestion pairs. for selecting suitable reviews for question generation, the ranker considers two factors: the major aspects in a review and the review's suitability for question generation. the two factors are captured via a reconstruction objective and a reinforcement objective with reward given by the generator. thus, the ranker and the generator are iteratively enhanced, and the adaptively transferred answer-question pairs and the augmented reviewquestion pairs gradually relieve the data lacking problem. in accordance with the second characteristic of our task, it is plausible to regard a review sentence or clause as the answer to the corresponding question originated from it. such treatment brings in the second challenge: how can we guarantee that the generated question concentrates on the critical aspect mentioned by the review sentence? for example, a question like "how was the experience for gaming?" is not a favourable generation for "i have battery life for more than two days for normal use, i.e. not power-consuming gaming.". to solve this problem, we incorporate aspect-based feature discovering in the ranker, and then we integrate the aspect features and an aspect pointer network in the generator. the incorporation of such aspect-related features and structures helps the generator to focus more on critical product aspects, other than the less important parts, which is complied with the real user-posed questions. to sum up, our main contributions are threefold. (1) a new practical task, namely question generation from reviews without annotated instance, is proposed and it has good potential for multiple applications. (2) a novel adaptive instance transfer and augmentation framework is proposed for handling the data lacking challenge in the task. (3) review-based question generation is conducted on e-commerce data of various product categories. ## related work question generation (qg) is an emerging research topic due to its wide application scenarios such as education @xcite , goal-oriented dialogue @xcite , and question answering @xcite . the preliminary neural qg models @xcite @xcite outperform the rule-based methods relying on hand-craft features, and thereafter various models have been proposed to further improve the performance via incorporating question type @xcite , answer position @xcite , long passage modeling @xcite , question difficulty @xcite , and to the point context @xcite . some works try to find the possible answer text spans for facilitating the learning @xcite . question generation models can be combined with its dual task, i.e., reading comprehension or question answering with various motivations, such as improving auxiliary task performance @xcite @xcite , collaborating qa and qg model @xcite , and unified learning @xcite . although question generation has been applied on other datasets, e.g., wikipedia @xcite , most of the existing qg works treat it as a dual task of reading comprehension @xcite , namely generating a question from a piece of text where a certain text span is marked as answer, in spite of several exceptions where only sentences without answer spans are used for generating questions @xcite . such generation setting is not suitable for reviews due to the lack of (question, review) pairs and improper assumption of text span answer as aforementioned. there are works training the question generation model with the user-written qa pairs in e-commerce sites @xcite , but the practicality is limited since the questions are only generated from answers instead of reviews. transfer learning @xcite @xcite refers to a broad scope of methods that exploit knowledge across domains for handling tasks in the target domain. a few terms are used for describing specific methods in this learning paradigm, e.g., self-taught learning @xcite , domain adaptation @xcite , etc. based on "what to transfer", transfer learning is categorized into four groups (pan and @xcite , namely instance transfer, feature representation transfer, parameter transfer, and relational knowledge transfer. our learning framework can be regarded as a case of instance transfer with iterative instance adaptation and augmentation. ## the proposed aita framework for handling the aforementioned issues, we propose an adaptive instance transfer and augmentation (aita) framework as shown in figure 1 . since the review-related processing is always sentencebased, we use "review" for short to refer to review sentence in this paper. its two components, namely ranker and generator, are learned iteratively. initially, aita simply transfers all available (question, answer) pairs and trains a generator. then it will iteratively enhance the generator with the help of the ranker. the ranker takes a (question, answer) pair and a review as its input and calculates a ranking score s. thus, it can rank all reviews for a given qa pair. the ranking objective incorporates the reward provided by the generator, which helps find out those suitable reviews to form (review, question) pairs for training (i.e. augmenting the training data). meanwhile, the reward from the generator also helps remove unsuitable qa pairs for training, so that it makes the transfer more adaptive. note that the ranker also learns to model two hidden aspect related variables for the review, which are helpful for the generator to ask about the major aspects in review. such an iterative instance manipulation procedure gradually transfers and augments the training set for handling review-based question generation. ## conclusions we propose a practical task of question generation from reviews, whose major challenge is the lack of training instances. an adaptive instance transfer and augmentation framework is designed for handling the task via an iterative learning algorithm. unsupervised aspect extraction is integrated for aspect-aware question generation. experiments on real-world e-commerce data demonstrate the effectiveness of the training instance manipulation in our framework and the potentials of the review-based question generation task.