ate_tk-instruct-base-def-pos-neg-neut-laptops

This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form:

• definition + 2 positive examples + 2 negative examples + 2 neutral examples

The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from laptops domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here.

For the ATE subtask, this model is the current SOTA.

Training data

InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels.

BibTeX entry and citation info

If you use this model in your work, please cite the following paper:

@inproceedings{Scaria2023InstructABSAIL,
  title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
  author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
  year={2023}
}