mlsanityData to 14 Oct 2026

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

arXiv 2109.03819 ↗First posted 7 Sep 2021cs.CL cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendations. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We proposed sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC ac-curacy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 7 Sep 2021.

Builds on

16 of 43 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  4. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  5. 2015Gated Graph Sequence Neural Networkscited by 796
  6. 2017Supervised Learning of Universal Sentence Representations from Natural Language Inference Datacited by 432
  7. 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
  8. 2019Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentencecited by 122
  9. 2020Relational Graph Attention Network for Aspect-based Sentiment Analysiscited by 104
  10. 2015Effective LSTMs for Target-Dependent Sentiment Classificationcited by 82
  11. 2017Graph Convolutional Encoders for Syntax-aware Neural Machine Translationcited by 70
  12. 2019Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classificationcited by 50
4 more
  1. 2019On Adversarial Removal of Hypothesis-only Bias in Natural Language Inferencecited by 29
  2. 2018Adaptive Semi-supervised Learning for Cross-domain Sentiment Classificationcited by 16
  3. 2019Improving Domain Adaptation Translation with Domain Invariant and Specific Informationcited by 7
  4. 2020Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulationcited by 7

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023LLM-augmented Preference Learning from Natural Languagecited by 1