Paper
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis
Introduction
This work focuses on labeled review generation in low-resource domains to improve the performance of review analysis tasks like ABSA.
Main Problem
There is a lack of fine-grained labeled data in many domains, which hinders the effectiveness of Aspect-Based Sentiment Analysis (ABSA). To alleviate this problem, this research proposes cross-domain review generation to transfer labeled reviews from one domain to another.
Illustrative Example
Source domain: Laptop
The [MacBook]positive is lightweight, but the [battery]negative never held a charge longer than 1 hour!
Target domain: Restaurant
The [fish soup] positive is delicious, but the [sushi]negative never tastes as good as before!
Input
A review sentence in source domain
Output
A review sentence in target domain
Motivation
The authors were motivated by the lack of fine-grained labeled data in many domains for ABSA tasks. Collecting such data is time-consuming and expensive. The goal was to develop an unsupervised domain adaptation method that could leverage labeled data from a source domain and generate labeled reviews in a target domain to reduce the dependency on manual annotation.
Related Works and Their Gaps
1) Feature-based adaptation (Blitzer et al., 2007; Glorot et al., 2011; Yu and Jiang, 2017; Ziser and Reichart, 2018; Ghosal et al., 2020): Focuses on domain-invariant features.
2) Instance-based adaptation (Mansour et al., 2008; Dredze et al., 2010): Focuses on rewriting labeled instances.
In the domain adaptation problem for ABSA (Ding et al., 2017; Wang and Pan, 2018; Li et al., 2019b; Gong et al., 2020), the following gaps exist:
Previous attempts lack interpretability and the supervision in transformation comes only from one domain. This research uses the information in the target domain to ensure supervision for labeling comes solely from the source domain. Both methods lack interpretability since the re-weighted instances offer little transparency regarding the transferred knowledge to the target domain.
Contribution of This Paper
This paper provides a way to avoid expensive and time-consuming annotation processes for ABSA. As long as the source domain has sufficient labeled data, the model can generate reviews in the target domain. Additionally, compared to others, this method provides some interpretable explanations for domain adaptation.
Proposed Methods
The procedure is as follows:
First, the system transfers the source domain reviews to domain-independent reviews (by masking source-related attributes). Then, it transfers the domain-independent review to the target domain using a pre-trained language model in the target domain. The challenge is that the data distribution between the source and target domains differs.
Experiments
Model: BERT
Datasets:
SemEval 14 Laptops
SemEval 14, 15, 16 Restaurants
Digital Device reviews by Toprak et al. (2010)
Services by Hu and Liu (2004)
Implementation
Not included
Gaps in This Work
How well the model handles unseen patterns and sentiments remains unclear. It may work well in similar domains, but in cases where review domains differ significantly, fine mapping may be difficult.
The challenge is that the data distribution between the source and target domains differs. I believe the final result comes from an intersection of patterns between the two domains. As a result, some patterns in the target domain may be missed, or certain patterns from the source domain may lose their meaning during the transformation process.
Paper Cross-Domain Review Generation for Aspect-Based Sentiment Analysis
Introduction This work focuses on labeled review generation in low-resource domains to improve the performance of review analysis tasks like ABSA.
Main Problem There is a lack of fine-grained labeled data in many domains, which hinders the effectiveness of Aspect-Based Sentiment Analysis (ABSA). To alleviate this problem, this research proposes cross-domain review generation to transfer labeled reviews from one domain to another.
Illustrative Example Source domain: Laptop The [MacBook]positive is lightweight, but the [battery]negative never held a charge longer than 1 hour! Target domain: Restaurant The [fish soup] positive is delicious, but the [sushi]negative never tastes as good as before!
Input A review sentence in source domain
Output A review sentence in target domain
Motivation The authors were motivated by the lack of fine-grained labeled data in many domains for ABSA tasks. Collecting such data is time-consuming and expensive. The goal was to develop an unsupervised domain adaptation method that could leverage labeled data from a source domain and generate labeled reviews in a target domain to reduce the dependency on manual annotation.
Related Works and Their Gaps 1) Feature-based adaptation (Blitzer et al., 2007; Glorot et al., 2011; Yu and Jiang, 2017; Ziser and Reichart, 2018; Ghosal et al., 2020): Focuses on domain-invariant features. 2) Instance-based adaptation (Mansour et al., 2008; Dredze et al., 2010): Focuses on rewriting labeled instances.
In the domain adaptation problem for ABSA (Ding et al., 2017; Wang and Pan, 2018; Li et al., 2019b; Gong et al., 2020), the following gaps exist: Previous attempts lack interpretability and the supervision in transformation comes only from one domain. This research uses the information in the target domain to ensure supervision for labeling comes solely from the source domain. Both methods lack interpretability since the re-weighted instances offer little transparency regarding the transferred knowledge to the target domain.
Contribution of This Paper This paper provides a way to avoid expensive and time-consuming annotation processes for ABSA. As long as the source domain has sufficient labeled data, the model can generate reviews in the target domain. Additionally, compared to others, this method provides some interpretable explanations for domain adaptation.
Proposed Methods The procedure is as follows: First, the system transfers the source domain reviews to domain-independent reviews (by masking source-related attributes). Then, it transfers the domain-independent review to the target domain using a pre-trained language model in the target domain. The challenge is that the data distribution between the source and target domains differs.
Experiments Model: BERT Datasets: SemEval 14 Laptops SemEval 14, 15, 16 Restaurants Digital Device reviews by Toprak et al. (2010) Services by Hu and Liu (2004)
Implementation Not included
Gaps in This Work How well the model handles unseen patterns and sentiments remains unclear. It may work well in similar domains, but in cases where review domains differ significantly, fine mapping may be difficult. The challenge is that the data distribution between the source and target domains differs. I believe the final result comes from an intersection of patterns between the two domains. As a result, some patterns in the target domain may be missed, or certain patterns from the source domain may lose their meaning during the transformation process.