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..
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations
Authors: Jihong Ouyang, Zhiyao Yang, Silong Liang, Bing Wang, Yimeng Wang, Ximing Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test ABSA-ESA on two ABSA benchmarks. The results show that ABSA-ESA outperforms the SOTA baselines on implicit and explicit sentiment accuracy. |
| Researcher Affiliation | Academia | Jihong Ouyang1,2*, Zhiyao Yang1,2*, Silong Liang1,2, Bing Wang1,2, Yimeng Wang1, Ximing Li1,2 1College of Computer Science and Technology, Jilin University, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE, Jilin University, China {ouyj@, zhiyaoy20@mails., liangsl23@mails.}jlu.edu.cn |
| Pseudocode | Yes | Algorithm 1: t-th Step Constrained Beam Search ... For clarity, Algorithm 1 provides the i-th CBS step process. |
| Open Source Code | No | The paper does not provide any statements about making its source code publicly available, nor does it include a link to a code repository for the methodology described. |
| Open Datasets | Yes | In our experiments, we use the dataset released by Li et al. (2021b), which has already been labeled with explicit and implicit sentiment. |
| Dataset Splits | No | The paper mentions 'train' and 'test' datasets in Table 1 and discusses training process details, but it does not specify explicit validation dataset splits (percentages or counts) or reference predefined validation splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory amounts) used to run the experiments. |
| Software Dependencies | No | The paper mentions models like T5 and BERT, but it does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow, or specific library versions) needed for reproducibility. |
| Experiment Setup | Yes | In the training process, we set the learning rate as 5 10 5 (Restaurant) and 2 10 5 (Laptop). The batch size is 4 (Restaurant) and 8 (Laptop). We set the training epoch as 15 for both datasets. We set kc=2 in data selection and kn=4 for Unlikelihood Contrastive Regularization. For Constrained Beam Search, we select V =6 and z=3. |