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 domainspecific 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.

heading: 1 Introduction
text: With the popularity of e-commerce platforms, many merchants publish various kinds of content about products on the Web (Khern-am nuai et al., 2023). 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
∗ Corresponding author.
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 (Feng et al., 2021). 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 (Deng et al., 2022). 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 (Pecar, 2018), 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 (Zhang et al., 2023), 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 (Li et al.,
9074
2022). That makes it hard to achieve reliable performance in the low-resource domains. A simple solution is knowledge transfer (Yu et al., 2021), which leverages the supervision from a rich source to supplement the poor one (Zou et al., 2021) by either pretrain-finetuning or multitask learning. However, both transfer methods require a large size of labeled data in the target domain (Zhao et al., 2022), 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 (Gu et al., 2021), 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.
The main contributions of this paper include,
• We reveal the issue of imbalanced domain resources in the field of SUBJPQA, and point out the challenges of adapting to low-resource domains, which are new for this task.
• We propose a new adaptive model based on adversarial disentangled learning that derives key latent factors to grasp domain generalization knowledge for low-resource SUBJPQA.
• We conduct extensive experiments on the popular dataset to evaluate the rationality and effectiveness of our approach.

heading: 2 Methodology
text: We first give some notations for this task. Let Ds = {c1, . . . , cls} denote the contexts of products, with ls utterances in the source domain, where ci = {xi,1, . . . , xi,lsi} is the i-th context with lsi words. We use the special symbol ⟨sep⟩ to separate contexts. The gold answer contains an objective part Y objs = {y1, . . . , ylo} and a subjective part Y subjs ={y1, . . . , yls}. Based on the target contexts Dt = {c1, . . . , clt}, our goal is to yield this inductive answer with correct Y objt and Y subj t with low labeled resource. Next, we illustrate the adaptive model for low-resource SUBJPQA in Figure 2, which includes knowledge retrieval and representations, disentangled learning, and target model adaption.

heading: 2.1 Knowledge Retrieval and Representations
text: To build a SUBJPQA model, we first need to acquire and represent the answer-related knowledge finely.
Subjective questions often involve various kinds of knowledge, such as product aspects, attributes, facts, opinions, and even implicit commonsense relations. For example, it is common sense that refresh rate is related to the phone screen. When users ask about the refresh rate, they actually want to know the quality of phone screen. Without capturing such knowledge, it is difficult to provide satisfactory answers. Thereby, we propose to use prompts to inquire about the external large language model (Nie et al., 2023), which is reported to contain rich implicit knowledge. This content can supplement the necessary but missing contexts of the inputs, enabling models to better derive the answer. Considering the content may contain irrelevant noise, we filter them by keyword matching (via the BM25 (Askari et al., 2023)) and semantic simi-
larity (via BERT (Zhang et al., 2019) model). Concretely, we use the BM25 score fbm25 and BERT embedding-based fbert to compute cosine similarity between product questions with other product contents. BM25 has high precision but low recall, while BERT has higher recall. By combining them, we can learn from each other to accurately acquire the relevant content with high recall. The final score is a weighted sum of two scores, as Eq.(1),
fscore = α · σ(fbm25) + (1− α) · fbert, (1)
where α is a hyperparameter and σ is the sigmoid function to transforms the fbm25 into a value between 0 and 1. The content with a score fscore below a threshold ε is viewed as noise and filtered.
Based on the retrieved contents, we then derive contextual representations for the samples. Each sample consists of product descriptions, a set of reviews, and implicit knowledge. We then encode each textual part by BART (Lewis et al., 2020) which is effective in capturing fine-grained context. We input the question and each textual part to compute their cross-attention (Roy and Kundu, 2023) which can emphasize the question-aware content. Next, we grasp correlations among the textual parts by an inter-attention mechanism (Li et al., 2023c). We incorporate the dependent context to update token representations. This is done by concatenating all context vectors and applying a linear transformation with a trainable matrix Wc, as Eq.(2),
hkτ = Wc · [ h
∥ i=1 ∑ s Ai(cs, ck)], (2)
where ∥ is the concatenating operation, Ai(·) is the attention function, hkτ is the k-th inter-attention vector. Finally, we fuse the vectors with cross-andinter attention to obtain a representation vector hτ .

heading: 2.2 Disentangled Learning for Adaptation
text: Different product domains have deviations in terms of feature patterns and label distributions, leading to the domain shift problem. In other words, the model trained in a source domain may be biased towards some subordinate features instead of key discriminant factors. The answer decoder will easily confuse to swing around two domains. That would deteriorate the performance and generalization ability of the model (Zhao et al., 2022), making it difficult to transfer to the low-resource domain (Chronopoulou et al., 2022). To handle this issue, we introduce a disentangled learning module that can separate latent factors, including the invariant factor shared across domains, and the one customized to a specific domain. In this latent space, we can narrow the gap between two domains and grasp the key cross-domain knowledge. That can improve the adaptability of the model and tackle the target data sparsity accordingly. (1) Disentanglement. We observe the vector hτ obtained from the previous step may be entangled with various biased correlations. To reduce the bias and enhance the model’s discriminability, we disentangle it into two independent latent factors, i.e., invariant hτinv and domain-specific h τ spe, as Eq.(3),
hτinv = Minv(hτ ), hτspe = Mspe(hτ ), (3)
where Minv and Mspe are multi-layer feedforward networks with a LeakyReLU activation function. (2) Reconstruction. To guide the disentangled direction, we regularize the distance of the two invariant vectors from the source and target domains, and let them be semantically closer. Simultaneously, we encourage their domain-specific vectors to be close in the same subspace. That allows the model to better transfer knowledge to the low-resource domain. In detail, we employ backtracked (Li et al., 2023a) and cross-domain (Yu et al., 2023a) reconstruction strategies. The first one is to restore the original representation hτ based on the disentangled vectors hτinv and h τ spe, as Eq.(4). The motivation is that a good disentanglement should maximally preserve information of the original vector.
h′τ = M(1)rec([hτinv;hτspe]), τ ∈ {s, t}, (4)
where τ denotes the source/target domain, hτinv and hτspe are the invariant and specific vectors, and h ′ τ is the reconstructed vector. Further, we use the source invariant vector hsinv and a domain-specific vector htspe from the target to reconstruct the source
original vector h′′s , as Eq.(5). That can reduce the discrepancy caused by different domain data, thus enabling new domain adaption.
h′′s = M(2)rec([hsinv;htspe]). (5)
To facilitate joint learning of disentanglement and reconstruction, we adopt a data augmentation technique, which can provide data to start the iterative process. Satisfactory disentangled vectors should be able to decode the answers effectively. Thus, we utilize the reconstructed vectors h′s, h ′′ s , h′t and h ′′ t to yield answers Ŷ obj s , Ŷ obj t in both the source and target domains. The reconstructed representations act as enhanced training signals to regularize the disentanglement. We define the augmented generation loss as the cross-entropy between the generated answers and the ground truth:
Ls,ωsaug = − ∑lso
t=1logP (yt|ŷ1, . . . , ŷ∗t−1, ωs), Lt,ωtaug = − ∑lto t=1logP (yt|ŷ1, . . . , ŷ∗t−1, ωt), (6)
where ωs ∈ {h′s, h′′s} and ωt ∈ {h′t, h′′t } indicate the representations under different reconstruction strategies, and ls/to is the length of the gold objective answer. The augmented loss Laug acts as an auxiliary training objective. That encourages the model to better learn transferable knowledge, so as to produce high-quality answers in both domains. (3) Discriminator. To learn the optional disentangled mapping function, we employ adversarial learning by using a discriminator to distinguish between the domain-specific representations (hsspe and htspe) from the source and target domains. This helps to reduce the distance in a shared subspace between representations of two domains, thus encouraging the reconstructed target distributions to be closer to the source one. In this way, the external domain data can be used to enhance the training of in-domain data. The discriminator is also implemented as a 3-layer feed-forward network. The logistic loss function is defined as Eq.(7).
Ldis = − ∑
τ∈{s,t} logPD(τ |hτspe), (7)
where PD(τ |hτspe) is the predicted probability of D that hτspe belongs to the source or target domains. (4) Contrastive Learning. To learn effective representation, we further employ contrastive learning (Gao et al., 2021) which is good at pulling semantically close neighbors together and pushing non-neighbors apart. The reconstructed vector is
expected to have similar semantics with the original representation (Chen et al., 2020). We thus take the original and reconstructed representations from the target domain and treat them as a positive pair. Then we contrast the positive pairs against negative samples from the training batch. That can push away dissimilar representations, which helps the model better learn domain-invariant knowledge. The contrastive loss is defined as Eq.(8).
L(1)cl = − ∑n i=1 log esim(h
(i) t ,h ′(i) t )/γ
∑ k 1[k ̸=i]e sim(h(i)t ,h ′(k) t )/γ ,
L(2)cl = − ∑n i=1 log esim(h
(i) t ,h ′′(i) t )/γ
∑ k 1[k ̸=i]e sim(h(i)t ,h ′′(k) t )/γ ,
(8) where 1 is the indicator function, sim(·) is a cosine similarity function, n is the number of batch size, and γ is the temperature parameter. In addition, the reconstruction vectors (h′s, h ′′ s ) in the source domain are expected to be similar, but dissimilar to all other instances in the same training batch. Thus, we have the loss as Eq.(9),
L(3)cl = − ∑n i=1 log esim(h ′(i) s ,h ′′(i) s )/γ
∑ k 1[k ̸=i]e sim(h′(i)s ,h ′′(k) s )/γ
. (9)
Based on the two contrastive learning strategies in Eq.(8) and Eq.(9), we can obtain more robust disentangled representations that effectively share common knowledge across domains while retaining domain-specificity. This allows better adaptation and performance for SUBJPQA in the low-resource domains. Finally, the contrastive loss over the training batch can be formulated as Eq.(10),
Lcl = L(1)cl + L (2) cl + L (3) cl . (10)
By combining adversarial learning and contrastive learning, the joint loss function for the domain adaptation part is defined as Eq.(11), where β1 and β2 are the hyper-parameters to adjust the joint loss.
L′ = β1Ldis + β2Lcl + (1− β1 − β2)Laug, Laug = ∑ τ∈{s,t} (Lτ,ωsaug + Lτ,ωtaug ). (11)

heading: 2.3 Multi-perspective Answer Generation
text: For the SUBJPQA task, a good answer that can meet users’ information needs should include two parts, including objective product facts and subjective opinions based on user reviews. We design a decoder to yield this multi-perspective inductive result. In particular, we first concatenate four kinds of
vectors to facilitate knowledge transfer, including the source invariant vector hsinv, the target invariant vector htinv, domain-specific vector h t spe and contextual vector ht from the source and target domain, respectively. By feeding them into a linear transformation, we can obtain hf . Based on it as input along with the previous words, the decoder produces the answer word-by-word. The decoder is learned separately through two parts: the objective part is trained via maximum likelihood estimation (MLE) on product facts. For the subjective part, we design a template according to the reviews’ sentiment distribution to cover diverse viewpoints with positive, negative, and neutral opinions. To train the whole model, we employ reinforcement learning (Yadav et al., 2021), with the following process: • Objective Part. To generate the objective part of the inductive answers, we utilize the disentangled vectors (htinv and h t spe) and contextual vectors ht to input the decoder. The model is trained based on the negative log-likelihood loss, as Eq.(12).
Lmle = − ∑l t=1 log(w ∗ t |w∗1, . . . , w∗t−1,hf ),
(12) where w∗j denotes the decoder output at the step j. • Subjective Part. We feed the fused representation hf into the decoder. Besides, we design two reward functions to encourage the decoder to learn the expected answering way. One is the sentiment recognition reward rsen which ensures the generated answers reflect diverse viewpoints with various sentiments. Another is the template-identified reward rtem which is introduced to help the verbalism of results comply with the pre-defined expressive structure in the template. The model is trained via policy gradient methods to maximize a mixed reward. We minimize the RL loss function as:
Lrl = −EAs∼pθ [r(As,A∗, T ∗)], (13)
where A∗ is the gold answer, As is the answer by sampling the words from the model’s output distribution, and T ∗ is the template. The overall reward r(As,A∗, T ∗) is the weighted sum of sentiment recognition and template identification rewards. Finally, we adopt policy gradient methods to train the RL-based answer decoder. The network is trained using the mixed loss as Eq.(14).
L′′ = ηLrl + (1− η)Lmle, (14)
where η is the scaling factor, which is used to balance the weights between the reward loss and the
maximum likelihood estimation loss. In this way, our model can produce comprehensive summaries with multi-perspective viewpoints as inductive answers for the low-resource domain.

heading: 3 Evaluations
text: We extensively evaluated the effectiveness of our method with quantitative and qualitative analysis.

heading: 3.1 Data and Experimental Settings
text: To evaluate our proposed method, we utilized a typical SupQA dataset (Zhang et al., 2023) in the field of SUBJPQA which contained 48, 352 samples across 15 product domains. Each sample included a subjective question, product descriptions, attributes, multiple user reviews with diversified sentiments, and a multi-perspective answer summarizing objective facts and subjective reviews. The dataset covered a diverse range of product categories with imbalanced distributions, enabling us to evaluate domain adaptation capabilities. This made it more suitable than other datasets to evaluate our task. For example, other PQA datasets (i.e., Amazon (Wan and McAuley, 2016), SubjQA (Bjerva et al., 2020), and AmazonQA (Gupta et al., 2019)) could only provide simple answers in one-side, which did not reflect the multi-viewpoint summarization to suit our task. Thus, the SupQA dataset was representative and can effectively evaluate the model. More details were given in Appendix A. We further used BLEU (Bn) (Papineni et al., 2002) and ROUGE (Rn) (Lin, 2004) to measure the generated quality of the inductive answer. These metrics were widely used in the field of text generation, where our answer is essentially a summary of multiple contents. We repeated running 10 times and reported the average performance to reduce bias. The metric values would be larger when the generated content can well resemble a human speaking way.
To simulate the low-resource scenarios, we selected minimum amounts, medium amounts, and maximum amounts (that is, 1%, 5%, and 10%, respectively) of training samples randomly. The samples from the Sports and Outdoors (SO) and Video Games (VG) were treated as target domains, while the full Electronics (ET) and Home and Kitchen (HK) as sources. That created an imbalanced distribution across domains, with sparse SO and VG.

heading: 3.2 Comparisons Against State-of-the-Arts
text: We compared our method against 8 mainstream models, including (1) BM25 (Robertson et al.,
2009) and LexRank (Erkan and Radev, 2004), which were typical retrieval-based methods and acquired highly relevant product-related contents for each question; (2) PGNet (See et al., 2017), a robust Seq2seq model that utilized a hybrid pointergenerator mechanism to copy words directly from the source text; (3) BART (Lewis et al., 2020), a strong denoising autoencoder for many generation tasks; (4) Pegasus (Zhang et al., 2020a), a powerful transformer-based model with good performance on both rich and low-resource summarization tasks; (5) InstructDS (Wang et al., 2023), a query-based summarization model with instructionfollowing capabilities to generate tailored summaries; (6) PlanSum (Amplayo et al., 2021) and FewSum (Bražinskas et al., 2020), two typical opinion summarization models, which generated the subjective answers by summarizing product reviews. We reimplemented these baselines following their original settings, as shown in Appendix B.
The inductive answer covers both the objective facts and subjective opinions of the product. As illustrated in Table 1, for the objective part of the answer, our model was superior to the benchmark method (i.e., BART) under various low-resource settings. Based on only 1% target data, our model obtained improvements of + 5.47 and + 6.50 in terms of BLEU-2 and ROUGE-L, respectively, as compared to InstructDS. When we increased the training data, our advantage was still significant. This indicated our learned disentangled representations could effectively transfer knowledge from the rich domain to the low-resource one. In addition, for the subjective part of the answer, our model still significantly outperformed the best baseline (i.e., InstructDS). That showed the effectiveness of our model on learning subjective knowledge. We observed finetuned models (i.e., PGNet, BART and Pegasus) fell short in comparison to the instructtuning model InstructDS. More results of other product domains are detailed in Appendix C.1.

heading: 3.3 Human Evaluations
text: Moreover, we conducted human evaluations to qualitatively evaluate the answer’s quality. To finely analyze the inductive answer, we employed four metrics, including Factness (Fact) and Accuracy (Acc) to measure its objective part, Comprehensiveness (Comp) and Template Compliance (Tcp) for subjective part. Fact evaluated the coverage of the answer-related facts, while Acc measured the accuracy of to-the-point facts in the answer.
Comp checked whether the answer could fully summarize various viewpoints, and Tcp assessed the fluency of the answer and its matching degree to the pre-defined template. A 3-point range was used for each metric, with [0, 0.33] being low, [0.34, 0.66] being medium, and [0.67, 1] being high. To avoid biases, we randomly sampled test cases to grade the judges by participants. We employed Randolph’s kappa to measure the inter-rater reliability. The kappa scores κ were all higher than 0.7, which indicated a good agreement. As shown in Table 2, our model significantly outperformed other baselines in terms of all metrics. That was consistent with the quantitative results in the previous section. These results reflected that our model could disentangle key latent factors to better transfer objective and subjective knowledge across product domains. More details were provided in Appendix C.2.

heading: 3.4 Ablation Studies
text: To analyze the contribution of each component in our model, we conducted ablation studies by removing four key modules from our framework one by one, including (1) DisM that dropped the disentangled learning module and solely relied on BART encoders; (2) ConL discarded the contrastive loss objective in Eq.(9); (3) RecM that removed back-
tracked and cross-domain reconstruction strategies; (4) DomD that threw away a domain discriminator.
As shown in Figure 3, removing the disentangled module caused the most significant performance drop, indicating it played a crucial role in transferring knowledge across domains. Besides, excluding other three modules also led to noticeable degradation. These results verified all components in our model were beneficial for obtaining key factors to build a robust model. More evaluations on the disentangled module and case study were illustrated in Appendix 3.5, 3.6, respectively.

heading: 3.5 Study of Disentangled Representation
text: Besides, we utilized the t-SNE (Van der Maaten and Hinton, 2008) algorithm to visualize the latent representations after the disentangled learning and reconstruction. We used ET and HK as richresource domains and SO and VG as low-resource domains. As presented in Figure 4, after disentanglement, the demarcation between the two domains was clearer. This separation enabled the model to finely capture key discriminant factors, thus enhancing the decoding ability. After reconstruction with adversarial and contrastive learning, the hidden subspaces gradually converged and became more aligned across domains. This validated the ef-
fectiveness of extracting invariant knowledge while retaining domain-specificity for domain transfer.

heading: 3.6 Case Studies and Discussions
text: To provide more insights into our model, we conducted case studies, including one from the ET source domain and another from a low-resource Sports and Outdoors (SO) domain. As shown in Table 5, our model can generate satisfactory inductive answers for subjective questions in the lowresource domain. With only 5% training data from the target SO domain, our model still can produce an inductive answer capturing the key product facts like weight limit and dimensions. That indicated effective transfer of factual knowledge from the source ET via the learned domain-invariant representations. For the subjective part of the answer,
our model roughly followed the template structure to aggregate diversified users’ opinions. The generated answers covered positive, neutral, and negative viewpoints on the suitability of the kayak. This showed our model could produce comprehensive answers aggregating multiple viewpoints.

heading: 4 Related Work
text: Product Question Answering (PQA) has received extensive attention in recent years (Deng et al., 2023). Earlier efforts try to answer the yes/no questions about users’ opinions (McAuley and Yang, 2016), such as “Is the user satisfied with the mobile phone?” That can be framed as opinion mining (Yu and Lam, 2018), and answered by classifying the opinion polarity. On the other hand, some researchers focused on the factoid questions, where
their answers were often text spans in the given document (Xu et al., 2019). To extract the answers, many methods based on machine reading comprehension (Cui et al., 2017) were proposed. Another method was based on retrieval, which treated PQA as an answer selection task (Yu et al., 2018). The documents were ranked from a set of candidates to select the most relevant one as results (Zhang et al., 2020b). In addition, some works proposed to generate the answers directly based on the Seq2seq framework (Gao et al., 2019). To answer the opinion questions, Deng et al. (2020) proposed to produce opinion-aware answers based on multi-task learning. To capture the fine-grained relations of opinions and facts, Feng et al. (2021) utilized a heterogeneous graph neural network to form subjective answers. Besides, there are other works on PQA, such as analyzing the semi-structured data (Shen et al., 2022), making cross-lingual prediction (Shen et al., 2023). Due to the lack of consideration of the answers’ structure, existing results often only cover either a piece of facts or opinions, which are not comprehensive.
Low-resource Generation(Yu et al., 2020) has gained significant attention in the literature (Yu et al., 2023c). Yu et al. (2021) was the first to study the domain adaptation (Li et al., 2023b) for generation. Chen and Shuai (2021) introduced an effective meta-transfer learning method for lowresource summarization. Besides, Cheng et al. (2023) proposed a reinforcement method to support generation in new domains with limited data. Sukhadia and Umesh (2023) designed a domain adaptive method, which used encodings of a pretrained ASR model as features to learn a target model. Calderon et al. (2022) presented a corruptand-reconstruct approach for generating domain counterfactuals and applied it as a data augmentation method. Zhao et al. (2022) explored a prompt learning model to handle zero-resource summariza-
tion. Zhang et al. (2022) developed a GAN-based framework for one-shot domain adaptation, leveraging a reference image and its binary entity mask to transfer pre-trained GAN styles and entities to a target domain with minimal data(Yu et al., 2023b). In contrast, we propose a new adaptive model that derives key latent factors with several constraints to grasp domain generalization knowledge, which helps to achieve low-resource adaption.

heading: 5 Conclusion
text: 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.

heading: 6 Acknowledgments
text: This work is supported by the National Natural Science Foundation of China (62276279, 62102463, 62372483, 62276280, U2001211, U22B2060), Guangdong Basic and Applied Basic Research Foundation (2024B1515020032), Research Foundation of Science and Technology Plan Project of Guangzhou City (2023B01J0001, 2024B01W0004), Tencent WeChat Rhino-Bird Focused Research Program (WXG-FR-2023-06), and Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, MNR (2024NRDZ01).
Limitations
The subjective induction QA task in a low-resource scenario is challenging. Although we had proposed an effective model, there is still room for improvement. Current model is few-shot but not zero-shot. Zero-shot learning for domain transfer remains an open challenge. That is a promising research direction to realize domain alignment without using any target labeled data. In addition, by analyzing our bad cases, the results still had some mistakes, such as temporal errors, typos, adverb errors, etc. These challenges will be studied in future works.
Ethics Statement
This paper aims to generate an inductive answer for the subjective question on products. Excluding the misusage scenarios, there are few or even no ethical issues with this technology. However, the framework is based on product reviews. It is possible to input some low-quality reviews related to the moral issue topics, resulting in some offensive results. We have taken into account this matter. With diverse viewpoints in the input reviews, we summarize their sentiment as positive, negative, and neutral. For each sentiment category, our model summarizes key aspects and attributes to yield answers. These answers contain knowledge from multiple perspectives, including relevant facts, overall sentiment, positive representative viewpoints, negative ones, and neutral ones, etc. In this classify-thensummarize way, we can aggregate heterogeneous and inconsistent viewpoints as a comprehensive answer. We observe that low-quality reviews are usually uninformative, coarse, and unimportant, while high-quality reviews are often informative and full of fine-grained details. Our summarizer is designed based on maximum salient information
coverage. That can help to aggregate the salient aspects of high-quality reviews, and alleviate the effect of low-quality content.

heading: A Statistic of the Dataset
text: There has existed a large-scale dataset SupQA tailored for SUBJPQA. As shown in Figure 6, the dis-
https://github.com/MetaZ1/ACL-SubjPQA
tribution of product categories in the SupQA is imbalanced with categories electronic, home, sports accounting for over 60% of the data. Categories such as beauty, clothing account for only 0.5%. The shortage of labeled data makes it challenging for models to achieve reliable performance on SUBJPQA in the low-resource product domains.

heading: B Additional Implementation Details
text: For evaluation, we reimplemented each baseline with default settings. For fair comparisons, we conducted five runs and showed the average results.
Ours: We implemented our experiments on two Nvidia RTX 3090 GPUs with PyTorch. We initialized our model with the pre-trained BARTbase provided by Hugging Face (Wolf et al., 2020). The training batch size was 4, and we used the Adam optimizer with a learning rate of 3e-5 for BARTbase related modules and 1e-3 for the contextual representation module. During the adaptation step, we used a learning rate of 3e-6 for MLE training, 3e-7 for RL training, and 3e-4 for the discriminators. Our disentangled and reconstruction modules were built by multi-layer perceptron and activator. We utilized the grid search to tune the hyper-parameters according to the validation performance. The trade-off parameters α, ε, β1, β2, and η were set to 0.3, 0.9, 0.25, 0.5, and 0.9, respectively. The LLM we used to yield implicit commonsense knowledge was the GPT-3.5 turbo . The sentiments of product reviews in Figure 2 were obtained via a review text classifier on Huggingface platform, which was fine-tuned on Amazon product reviews corpus.
PGNet: For training the pointer-generator network model, we utilized a bidirectional two-layer LSTM as the encoder and a uni-directional singlelayer LSTM as the decoder. The hidden size was
https://platform.openai.com/docs/guides/text-generation
set to 512 to capture richer context representations. We initialized both models with 300-dimensional GloVe word embeddings pre-trained on a large corpus. To prevent overfitting, we set the maximum number of epochs to 30 and employed an early stopping strategy with a patience of 5 epochs. The gradient norm was clipped at 5.0 to stabilize training. The batch size was 64 for efficient training on GPUs, and we used the Adam optimizer with a learning rate of 2e-4 and a weight decay of 1e-5.
BART: We initialized the model with base size pre-trained on a large corpus for stronger language modeling capabilities. The maximum number of epochs was 20 with early stopping after 3 epochs if the validation loss does not decrease. The batch size was 32, and we utilized the AdamW optimizer with a linear warmup and decay schedule. The peak learning rate was 5e-5 and the weight decay was 1e-2 to regularize training objectives.
Pegasus: We reimplemented the model based on the transformers library with configurations for abstractive summarization tasks. The model featured a vocabulary size of 50,265 and utilizes 1024 position embeddings to handle long product-related content. It was composed of 12 layers each for the encoder and decoder, with a hidden size of 1024 and an FFN dimension of 4096. The model used 16 attention heads in both the encoder and decoder. We employed GELU as its activation function, with a dropout rate of 0.1 and an attention dropout of 0.0 to prevent overfitting. The model initialization standard deviation was set at 0.02, and it includes parameters for padding, end-of-sequence, and forced end-of-sequence tokens. For optimization, both pre-training and fine-tuning used Adafactor (Shazeer and Stern, 2018) with square root learning rate decay and dropout rate of 0.1.
InstructDS: The model was built on the foundation of Flan-T5-XL (Chung et al., 2022), leveraging LORA for parameter-efficient training, resulting in 37.7 million trainable parameters. This model was unique in its approach to dialogue summarization, aiming to synthesize high-quality query-based summarization triples by exploiting the question generation and answering capabilities of large language models. It underwent instruction tuning with a focus on general summarization, query-based summarization, and length-aware augmentations, making it adept at producing summaries that were tailored to user instructions and preferences.
For the PlanSum and FewSum subjective models, we followed the settings described in their original papers to ensure a fair comparison.
In the validation phase, we evaluated the loss for each epoch. When the loss was minimum, we can derived an optimal model for evaluation.

heading: C Additional Evaluations
text: Due to the page limit, we showed additional experiments as follows, including the comparison results, human evaluation, disentangled module analysis, case study, and other impact aspects.
C.1 Extra Performance Comparisons
In this section, we show additional performance in various domains, including ET domain to VG domain, HK domain to SO domain, and HK domain to VG domain. As shown in Table 3, Table 4 and Table 5, we have the following observations:
• In terms of the evaluation metrics BLEU-2 and ROUGE-L, our model significantly outperformed all baselines in different low-resource scenarios (using 1%, 5%, 10% target training data), for both objective and subjective part of the inductive answers. This demonstrated our model could effectively learn transferable knowledge from the source domain and apply it to the target domain. • Even in the extremely low-resource case (only 1% target data), our model still obtained considerable improvements compared to other baselines. As available training data increased, the advantage of our model still remained quite significant. That further verified the efficacy of our disentangled representation learning.
C.2 Human Evaluation Settings
To ensure reliable and unbiased human evaluation, we recruited 8 undergraduate students majoring in computer science as annotators. All of them are good at English and have strong language skills. We conducted qualification tests on their annotation abilities before recruitment. Each annotator evaluated around 250 randomly sampled answers, with over 2,000 annotated answers in total. Comprehensive guidelines were provided detailing the scoring criteria, scales, and examples. The key metrics included Factness, Accuracy, Comprehensiveness, and Template Compliance. We calculated inter-annotator agreement using Randolph’s kappa
https://github.com/rktamplayo/PlanSum https://github.com/abrazinskas/FewSum
to ensure consistency. The agreement scores were 0.81 for Factness, 0.79 for Accuracy, 0.77 for Comprehensiveness, and 0.74 for Template Compliance, indicating reliable annotations. To avoid bias, all evaluated samples were randomly shuffled before annotation. We also monitored the process and discussed disagreements to minimize errors.
C.3 Extra Human Evaluation Results In this section, we add additional human evaluation analysis to validate the performance of our proposed model, including ET domain to VG domain, HK domain to SO domain, and HK domain to VG domain. As shown in Table 6, Table 7 and Table 8, we have the following observations:
• All results across multiple low-resource scenarios demonstrated the effectiveness of our model for SUBJPQA. Compared to the strong baselines of Pegasus and InstructDS, our improvements were consistent and significant, especially in the sparse 1% training data setting.
• In terms of the objective metrics of Fact and
Acc, the proposed model achieved substantially higher scores. For example, with 1% target training data, the Acc score improved by 0.08 ∼ 0.11 over InstructDS. This indicated the model’s ability to generate accurate objective answers by effectively transferring domain-invariant knowledge from rich source domains. That reflected the efficacy of the cross-domain knowledge adaptation. • Similarly, for the subjective answers metrics of Comp and Tcp, the proposed model outperformed baselines by a large margin. Even with only 1% target data, the comprehension score surpassed InstructDS by 0.05 ∼ 0.12, showing the model’s capacity to produce comprehensive subjective answers covering multi-perspective viewpoints. The significant boost in Tcp demonstrated the effectiveness of reinforcement learning. • Moreover, consistent trends could be observed across different low-resource domain pairs like Electronics-Sports, Electronics-Video Games, etc. That indicated the robustness of our model.

heading: Future Work
text: These challenges will be studied in future works