Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis
Authors: Zhengxuan Wu, Desmond C. Ong14094-14102
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train both models with pretrained BERT on two (T)ABSA datasets: Senti Hood and Sem Eval-2014 (Task 4). Both models achieve new state-of-the-art results with our QACG-BERT model having the best performance. Furthermore, we provide analyses of the impact of context in the our proposed models. |
| Researcher Affiliation | Collaboration | Zhengxuan Wu 1, Desmond C. Ong 2, 3 1 Symbolic Systems Program, Stanford University 2 Department of Information Systems and Analytics, National University of Singapore 3 Institute of High Performance Computing, Agency for Science, Technology, and Research, Singapore |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 2 provides an illustration of the proposed models, but it is a diagram, not pseudocode. |
| Open Source Code | Yes | 1 https://github.com/frankaging/Quasi-Attention-ABSA |
| Open Datasets | Yes | For the TABSA task, we used the Sentihood dataset 5 which was built by questions and answers from Yahoo! with location names of London, UK. (Footnote 5: https://github.com/uclnlp/jack/tree/master/data/sentihood) For the ABSA task, we used the dataset from Sem Eval-2014 Task 4 6, which contains 3,044 sentences from restaurant reviews. (Footnote 6: http://alt.qcri.org/semeval2014/task4/) |
| Dataset Splits | Yes | Each dataset is partitioned to train, development and test sets as in its original paper. |
| Hardware Specification | Yes | We used a single Standard NC6 instance on Microsoft Azure, which is equipped with a single NVIDIA Tesla K80 GPU with 12G Memory. |
| Software Dependencies | No | The paper mentions using 'pretrained weights from the uncased BERT-base model' (footnote 7: https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-12_H-768_A-12.zip) but does not provide specific version numbers for other ancillary software dependencies like programming languages (e.g., Python), deep learning frameworks (e.g., PyTorch, TensorFlow), or other libraries used for implementation. |
| Experiment Setup | Yes | Our models consists of 12 heads and 12 layers, with hidden layer size 768. ... We trained for 25 epochs with a dropout probability of 0.1. The initial learning rate is 2e 5 for all layers, with a batch size of 24. |