Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (2022)

Abstract: Recently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation cost. To this end, we focus on multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging. We first design a dynamic feature network to fully exploit the extracted pre-trained features for efficient domain adaptation. Meanwhile, with the difference of the traditional source-target domain alignment methods, we propose a novel asymmetric mutual learning strategy, which can robustly estimate the pseudo-labels of the target domain with the knowledge from all the other source models. Experiments on multiple sentiment benchmarks show that our method outperforms the recent state-of-the-art approaches, and we also conduct extensive ablation studies to verify the effectiveness of each the proposed module.

## introduction sentiment classification @xcite aims to predict the sentiment label for each textual data automatically @xcite , which is one of the most popular natural language processing (nlp) tasks with many important applications, such as social media monitoring @xcite , market research @xcite , conversation sentiment detection @xcite , etc. very recently, the pre-trained language models (prlms), e.g., bert @xcite , have demonstrated significant improvements on widerange of nlp tasks, including the sentiment classification. this framework includes two steps: the transformer-based @xcite model is first pre-trained on large unlabeled corpus, and then fine-turned on the labeled datasets for the downstream tasks. however, as illustrated in figure 1(top), different domain texts often contain different subject words, and have different sentiment descriptions, which lead to decreased performance induced by @xcite (figure 1(bottom)). unsupervised domain adaptation (uda) is another hot research topic in machine learning to address the domain-shift. it aims to transfer the knowledge from the source domain to the target domain with the labeled source data and unlabeled target data. this is often achieved by minimizing a specific distance between the source and target domains to learn the shared domain-invariant features. for example, @xcite exploit several distance metrics (e.g., maximum mean discrepancy (mmd) @xcite alignment (coral) @xcite for domain adaptation in the context of text classification tasks. while, @xcite use a domain classifier to obtain domain-invariant sentiment features via adversarial training @xcite between the source and target features. despite the progress of recent cross-domain sentiment analysis works which mainly focus on single-source domain adaptation setting, it is still cumbersome to apply these domain alignment methods on the multi-source sentiment adaptation tasks, which is more practical in real-world scenarios. simply combining all the source domains into a single dataset may deliver worse performance compared with the best result from one of source dataset @xcite , due to the various source distributions. meanwhile, with the number of source domains increasing, the corresponding computation cost and complexity will be dramatically increased @xcite . in particular, the recent dominant frameworks prlms @xcite ) are adopted as the backbone for feature extraction, which usually contains a large amount of training parameters @xcite . therefore, there is a strong motivation to develop an efficient multi-source unsupervised sentiment adaptation framework which can generalize well to the target domain with no labeled target data. recent self-training methods @xcite @xcite achieve advanced performance on many unsupervised domain adaptation tasks by iteratively updating the pseudo-labels of the target data with current adapted model, and the model can be retrained with these self-annotated data. however, the pseudo-labels are not always reliable due to the distribution shift between the source domain and the target domain, and the incorrect pseudo-labels can significantly hurt the final adaptation performance. several techniques are proposed to reduce the negative effect of noisy pseudo labels, such as high-confidence threshold @xcite , self-ensemble bootstrapping @xcite , mutual information maximization @xcite , etc., which demonstrate improved performance on single-source cross-domain adaptation tasks. in this paper, we propose a novel asymmetric mutual learning (aml) strategy to estimate the pseudolabels robustly, and we show this strategy is wellsuited to the unsupervised multi-source domain adaptation setting. specifically, we design a classification model for each source domain. for each source model, the pseudo-labels of target data are derived from the ensembles of all the other source models. in contrast with traditional deep mutual learning @xcite which distills the knowledge of a single dataset with multiple models, our aml can utilize the knowledge from multiple datasets under different distributions. therefore, each source model can be enhanced with the other source models. unlike traditional self-training methods which generate pseudo-label by itself, our aml is more robust to the noisy pseudo-label. in addition, we tend to use the features extracted from bert for efficient sentiment adaptation, and this feature-based adaptation method is more memory-friendly compared with fine-tuning bert. to fully exploit bert features, we propose a dynamic network @xcite for better aggregating the features from different layers, which is referred to dynamic feature networks (dfn). compared with attention-based fusion @xcite which only scales the features, our dfn can dynamically adjust the parameters of the network according to each instance input for better performance. together with the two proposed modules (aml and dfn), we achieve new state-of-the-art performance on the widelyused sentiment benchmarks @xcite under unsupervised multi-source setting. we summarize our contributions as follows: the remainder of the paper is organized as follows. section 2 introduce the related work, followed by the proposed framework in section 3. experimental results are reported in section 4. conclusion is drawn in the last section 5. ## related work in this section, we mainly focus on recent related methods based on deep neural networks (dnns) due to their superior performance. sentiment classification: aims to predict the sentiment polarity of a given texts. dang et al. (2020) compare many dnn-based methods, such as convolutional neural networks (cnns) @xcite , recurrent neural networks (rnns) @xcite , etc. however, these methods often use word embedding or tf-idf as the representations of the texts, which can not capture the context information within a sentence. recently, with the advent of pre-trained language models which achieve impressive performance in many nlp tasks @xcite , more and more works adopt these prlms as the backbone for sentiment analysis @xcite . despite their great success, the performance of these models is still suffering from domain-shift of the datasets @xcite . unsupervised domain adaptation: is an attractive topic for dealing with the domain-shift problem. the mainstream is to reduce the distribution discrepancy between the source and the target domains @xcite . for sentiment classification tasks, some previous works aim to identify domain-invariant pivot words @xcite . however, pivot words identification is tedious and may be inaccurate. @xcite and @xcite tend to minimize the whole sentence representation by a binary domain classifier. as for the more challenging multi-source adaptation setting, mixture-ofexperts @xcite aligns the each domainpair based on mmd for simplicity, and ensemble all the source prediction based on the distance metric. while, @xcite uses a multi-class domain classifier to align multi-domain distributions @xcite incorporates pseudo-labels to further improve the performance. @xcite share a similar idea but using bert as the backbone. in contrast to most domain-alignment methods which become complex with the number of source domains increasing, we turn to self-training methods which demonstrate effective performance for uda @xcite @xcite , and the asymmetric mutual learning (aml) is proposed for robust pseudo-label generation in multi-source adaptation setting. dynamic networks: aim to adjust the networks' architectures or parameters conditioned on each input @xcite . skipnet @xcite can decide whether a block is kept and not, which can significantly reduce the inference time. condconv @xcite can select the best combination of the convolution parameters dynamically, which increase model capacity with marginal computation cost. dycnn @xcite shares a similar idea, while uses softmax function to derive the attention coefficiency for each convolution kernel. in this paper, we are interested in adapting the features extracted from bert for simplicity and efficiency. thus, we propose a dynamic feature network (dfn) to fully exploit the features and adjust the network parameters accordingly for better performance. ## method in this section, we first introduce the overall framework for multi-source unsupervised sentiment adaptation. next, we provide further details of each proposed module. the detailed training procedures are presented in the last section. ## experiments in this section, we extensively evaluate our model on two widely-used sentiment adaptation benchmarks, amazon view datasets foot_0 and skytrax view datasets foot_1 . first, we introduce the datasets, experiment setup, and implementation details. then, the performance of recent state-of-the-art adaptation methods are reported for comparisons. besides, we also conduct detailed ablation studies to verify the contribution of each proposed module. ## conclusion in this paper, we propose a novel framework for multi-source unsupervised domain adaptation on sentiment classification. to achieve efficient adaptation with the recent large-scale and powerful pretrained bert model, we propose a dynamic feature network to find the optimal network parameters for better features exploitation. besides, instead of explicitly reducing the distribution discrepancy between domain pairs which becomes complex with the number of source domain increasing, we design a asymmetric mutual learning strategy to estimate the pseudo-label of the target data directly. we conduct extensive experiments and ablation studies that verify the effectiveness and superiority of our proposed model.