Topic Driven Adaptive Network for Cross-Domain Sentiment Classification

• URL: http://arxiv.org/abs/2111.14094v1
• Date: Sun, 28 Nov 2021 10:17:11 GMT
• Title: Topic Driven Adaptive Network for Cross-Domain Sentiment Classification
• Authors: Yicheng Zhu, Yiqiao Qiu, Yanghui Rao
• Abstract summary: We propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: semantics attention network and domain-specific word attention network. Experiments validate the effectiveness of our TDAN on sentiment classification across domains.
• Score: 6.196375060616161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from the source domain and evaluate it on the target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: semantics attention network and domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains.

Related papers

• DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting [35.83485358725357]
  Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. Existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset. We propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains.
  arXiv  Detail & Related papers  (2023-08-10T02:59:40Z)
• Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation [14.623272346517794]
  Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Most existing UDA approaches enable knowledge transfer via learning domain-invariant representation and sharing one classifier across two domains. We propose a Win-Win TRansformer framework (WinTR) that separately explores the domain-specific knowledge for each domain and interchanges cross-domain knowledge.
  arXiv  Detail & Related papers  (2021-11-25T06:45:07Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation [67.83872616307008]
  Unversarial Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain. In this paper, we propose a novel Adrial Dual Distincts Network (AD$2$CN) to align the source and target domain data distribution simultaneously with matching task-specific category boundaries. To be specific, a domain-invariant feature generator is exploited to embed the source and target data into a latent common space with the guidance of discriminative cross-domain alignment.
  arXiv  Detail & Related papers  (2020-08-27T01:29:10Z)
• Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment [11.74643883335152]
  Unsupervised domain adaptation is a promising avenue which transfers knowledge from a source domain to a target domain. We propose distance metric guided feature alignment (MetFA) to extract discriminative as well as domain-invariant features on both source and target domains. Our model integrates class distribution alignment to transfer semantic knowledge from a source domain to a target domain.
  arXiv  Detail & Related papers  (2020-08-19T13:36:57Z)
• Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation [56.94873619509414]
  Conventional unsupervised domain adaptation studies the knowledge transfer between a limited number of domains. We propose a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature disentanglement and Gram matrix. We demonstrate that our embedding is capable of predicting domain similarities that match our intuition about visual relations between different domains.
  arXiv  Detail & Related papers  (2020-07-17T22:05:09Z)
• Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation [9.251407403582501]
  This paper focuses on the type of open set domain adaptation setting where the target domain has both private ('unknown classes') label space and the shared ('known classes') label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting. We propose a novel adversarial domain adaptation model with multiple auxiliary classifiers.
  arXiv  Detail & Related papers  (2020-07-01T11:23:07Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
  Unsupervised domain adaptation is critical in various computer vision tasks. We design a new spatial attention pyramid network for unsupervised domain adaptation. Our method performs favorably against the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-03-29T09:03:23Z)
• Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning [51.742040588834996]
  Domain-adapted sentiment classification refers to training on a labeled source domain to well infer document-level sentiment on an unlabeled target domain. We propose a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers.
  arXiv  Detail & Related papers  (2020-02-01T01:22:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.