Domain Adaptation for Subjective Induction Questions Answering on Products by Adversarial Disentangled Learning (2024)

Abstract: This paper focuses on answering subjective questions about products. Different from the factoid question with a single answer span, this subjective one involves multiple viewpoints. For example, the question of ‘how the phone’s battery is?’ not only involves facts of battery capacity but also contains users’ opinions on the battery’s pros and cons. A good answer should be able to integrate these heterogeneous and even inconsistent viewpoints, which is formalized as a subjective induction QA task. For this task, the data distributions are often imbalanced across different product domains. It is hard for traditional methods to work well without considering the shift of domain patterns. To address this problem, we propose a novel domain-adaptive model. Concretely, for each sample in the source and target domain, we first retrieve answer-related knowledge and represent them independently. To facilitate knowledge transferring, we then disentangle the representations into domain-invariant and domain-specific latent factors. Moreover, we develop an adversarial discriminator with contrastive learning to reduce the impact of out-of-domain bias. Based on learned latent vectors in a target domain, we yield multi-perspective summaries as inductive answers. Experiments on popular datasets show the effectiveness of our method.

## introduction with the popularity of e-commerce platforms, many merchants publish various kinds of content about products on the web @xcite . based on such a large amount of content, it is difficult for consumers to seek useful knowledge for making informed purchase decisions. to tackle this information overload problem, consumers turn to ask questions on product attributes, functions, and user experiences via the forums. since it is intractable to manually reply to so many questions, product question-answering systems (pqa) have emerged. among these questions, the factoid ones have been well studied @xcite . for instance, for a factoid question "what is the operating system of this laptop?" we can simply extract a span "macos ventura" from the input text as the answer. however, many complex questions are still less investigated, such as the subjective ones asking about personal feelings, needs, and preferences @xcite . as shown in figure 1 , the question asks the performance of the nikon binocular in a low-light environment. a simple answer about the fact "wide angle" or one-sided opinion "i think it's alright" is hard to satisfy the various information needs of users. the users expect a good answer that can not only include relevant facts from the specifications of binoculars, but also cover both positive, neutral, and negative opinions from multiple perspectives. that provides them with a full understanding of product details and viewpoints. answering this subjective question in an inductive way can be formalized as a challenging task @xcite , which is called subjective induction qa, i.e., subjpqa. different from the factoid question, the answer in this task is more comprehensive, with multiple facts and diversified viewpoints from multiple data sources. it is hard for traditional extractive methods to integrate them. in addition to multi-source heterogeneous summarization, this task has difficulties in data scarcity and domain imbalance. in real-world applications, some domains have rich labeled resources while others have few. for example, in the supqa dataset @xcite , the categories electronic, home, sports account for over 60% of the data, while the domains of beauty, clothing account for only 0.5%. it is labor-intensive to acquire data in all domains and annotate them. considering existing methods are data-driven, insufficient labeled data would lead to under-training @xcite i'm looking for a binocular that is exceptionally good in low light to watch feeding owls at dusk and after. how do these 10x 50's perform? subjective product question ? ?? i found that they were too hard to stabilize, and the focus mechanism too slow with too much resistance. in the end, the extra magnification provided little advantage as the image was not any clearer than the 8*40, and the image was much less stable. negative reviews neutral reviews not the best. definitely there are worse but there are better available if that is your main purpose … but perform exceptionally well in high light conditions. these binoculars are quite good … they have clear view and easy handling. i use these binoculars for hunting. i find them very usefull for the price also in low light. i use these binoculars for astronomy, the ultimate low light challenge. not quite as good as high end binoculars, but excellent performance for the price. 2022). that makes it hard to achieve reliable performance in the low-resource domains. a simple solution is knowledge transfer @xcite , which leverages the supervision from a rich source to supplement the poor one @xcite by either pretrain-finetuning or multitask learning. however, both transfer methods require a large size of labeled data in the target domain @xcite , but in reality, there is often very little target data. although we can do pre-training with the help of large language models, it requires careful design of prompts for each domain, which is labor-intensive. moreover, the context patterns and feature distributions are different between various domains. for example, 'screen' and 'battery' are common in the electronics domain, whereas 'fabric' and 'fit' are often used in the clothing domain. this domain shift problem would lead to a performance drop across different domains @xcite , especially when generating our domain-related answers that involve the induction of multi-viewpoints. to address these challenges, we propose a new self-adaptive model for the subjpqa task. the motivation is that we can disentangle some domainagnostic knowledge from a large amount of source data to enhance the target generation under certain conditions. in detail, for each sample, we first acquire all answer-related implicit knowledge and encode their textual features. two encoders are used for the source and target domains, respectively. to support knowledge transfer, the parameters of the source encoder are used to initialize the target one. we then project each representation into a latent space to disentangle two key factors. one is the invariant factor, which captures some inductive patterns and semantic expressions common to most domains; the other is the domain-specific factor to grasp the aspects and attributes unique to a certain domain. such invariant representations enable us to flexibly reduce the domain impact when transferring from a rich source to a low-resource target. to support domain adaptation, we design three criteria for disentangled learning. the first one is based on reconstruction, where the disentangled representations should maximally preserve the information integrity. the second one is via adversarial learning. that is, a discriminator is used to distinguish the representations from different domains, while the encoders try to fool it inversely. moreover, we use another criterion of contrastive learning to ensure that the reconstructed representations are valid. by multi-criteria joint learning, we can reduce the domain deviation to learn robust representations that can generalize well to long-tail domains. finally, we use the learned latent vectors from the target domain to generate multi-perspective summaries as the answers for the subjpqa task. extensive experimental results from popular datasets demonstrate the effectiveness of our approach. we first give some notations for this task. let d @xmath0, . ## evaluations we extensively evaluated the effectiveness of our method with quantitative and qualitative analysis. ## related work product question answering (pqa) has received extensive attention in recent years @xcite . earlier efforts try to answer the yes/no questions about users' opinions @xcite , such as "is the user satisfied with the mobile phone?" that can be framed as opinion mining @xcite , and answered by classifying the opinion polarity. on the other hand, some researchers focused on the factoid questions, where ## conclusion this paper studied the task of subjective question answering on products (subjpqa). we revealed the issue of domain bias and imbalance, where the patterns and data distributions would vary in different domains. that posed challenges for traditional methods to achieve reliable performance on low-resource domains. to tackle this problem, we proposed to transfer knowledge from a rich domain to a poor one, and designed a novel selfadaptive model to facilitate transferring based on adversarial disentangled learning. in particular, for each instance in the source and target domain, we first retrieved answer-related knowledge and represented their contexts. we then disentangled their representations into domain-invariant and domainspecific latent vectors. to guide the disentangled direction, we designed the backtracked and crossdomain reconstruction constraints, which can regularize the results to maximally preserve the original data characteristics and capture their cross-domain correlations. moreover, we developed an adversarial discriminator with contrastive learning to reduce the impact of out-of-domain bias. based on learned latent vectors for the target domain, we decoded summaries multi-perspective viewpoints as inductive answers for subjpqa. extensive experiments on the typical dataset demonstrated the effectiveness of our method in low-resource settings.