Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets. However, existing methods are limited to the in-domain setting with two domains. Hence, we propose a domain-expanded benchmark to address the in-domain, out-of-domain and cross-domain settings. We support the new benchmark by annotating more than 4000 data samples for two new domains based on hotel and cosmetics reviews. Our analysis of five existing methods shows that while there is a significant gap between in-domain and out-of-domain performance, generative methods have a strong potential for domain generalization. Our datasets, code implementation and models are available at https://github.com/DAMO-NLP-SG/domain-expanded-aste .

[1]  Lidong Bing,et al.  A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges , 2022, IEEE Transactions on Knowledge and Data Engineering.

[2]  Lidong Bing,et al.  Aspect Sentiment Quad Prediction as Paraphrase Generation , 2021, EMNLP.

[3]  Lidong Bing,et al.  Towards Generative Aspect-Based Sentiment Analysis , 2021, ACL.

[4]  Lidong Bing,et al.  Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction , 2021, ACL.

[5]  Xipeng Qiu,et al.  A Unified Generative Framework for Aspect-based Sentiment Analysis , 2021, ACL.

[6]  Siva Reddy,et al.  Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval , 2021, EMNLP.

[7]  James R. Glass,et al.  Cooperative Self-training of Machine Reading Comprehension , 2021, NAACL.

[8]  Stefano Soatto,et al.  Structured Prediction as Translation between Augmented Natural Languages , 2021, ICLR.

[9]  Wai Lam,et al.  A Theoretical Analysis of the Repetition Problem in Text Generation , 2020, AAAI.

[10]  Mirella Lapata,et al.  Extractive Opinion Summarization in Quantized Transformer Spaces , 2020, Transactions of the Association for Computational Linguistics.

[11]  Jianfei Yu,et al.  Unified Feature and Instance Based Domain Adaptation for End-to-End Aspect-based Sentiment Analysis , 2020, EMNLP.

[12]  Xinyu Dai,et al.  Grid Tagging Scheme for End-to-End Fine-grained Opinion Extraction , 2020, FINDINGS.

[13]  Lu Xu,et al.  Position-Aware Tagging for Aspect Sentiment Triplet Extraction , 2020, EMNLP.

[14]  Rada Mihalcea,et al.  Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research , 2020, IEEE Transactions on Affective Computing.

[15]  Luo Si,et al.  Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis , 2019, AAAI.

[16]  Qiang Yang,et al.  Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning , 2019, EMNLP.

[17]  Omer Levy,et al.  BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension , 2019, ACL.

[18]  Colin Raffel,et al.  Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..

[19]  Sinno Jialin Pan,et al.  Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction , 2018, ACL.

[20]  Jeremy Barnes,et al.  MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification , 2018, LREC.

[21]  Geoffrey French,et al.  Self-ensembling for visual domain adaptation , 2017, ICLR.

[22]  Ming Zhou,et al.  Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction , 2016, IJCAI.

[23]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.

[24]  Shafiq R. Joty,et al.  Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings , 2015, EMNLP.

[25]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[26]  Suresh Manandhar,et al.  SemEval-2014 Task 4: Aspect Based Sentiment Analysis , 2014, *SEMEVAL.

[27]  Ming Zhou,et al.  Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification , 2014, ACL.

[28]  Philipp Cimiano,et al.  Joint and Pipeline Probabilistic Models for Fine-Grained Sentiment Analysis: Extracting Aspects, Subjective Phrases and their Relations , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[29]  Claire Cardie,et al.  Extracting Opinion Expressions with semi-Markov Conditional Random Fields , 2012, EMNLP.

[30]  Qiang Yang,et al.  Cross-Domain Co-Extraction of Sentiment and Topic Lexicons , 2012, ACL.

[31]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[32]  C. Hauff,et al.  Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training , 2022, NAACL-HLT.

[33]  Rui Xia,et al.  Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction , 2022, NAACL.

[34]  Shu Liu,et al.  A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction , 2022, NAACL.

[35]  Tieyun Qian,et al.  Bridge-Based Active Domain Adaptation for Aspect Term Extraction , 2021, ACL.

[36]  Rui Xia,et al.  Cross-Domain Review Generation for Aspect-Based Sentiment Analysis , 2021, FINDINGS.

[37]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[38]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.