• We will be adding LoRA and PTuning based finetuning methods for parameter efficient finetuning.
• We will be updating the task list to add 3 new tasks viz. aspect based opinion extraction (ABPE), aspect opinion pair extraction (AOPE) and aspect opinion sentiment triplet extraction (AOSTE).
• Revisions to paper to include more systematic ablation studies.

This repository is the official implementation of the paper. As part of our approach we show the efficacy of instruction tuned language models. This approach surpasses previous SOTA on downstream ABSA subtasks by significant margin.

This section describes the format of the data required for the training and evaluation of the datasets using our approach. For all subtasks, the field names should match exactly as shown and have the same datatypes. The fields to be present in the raw dataset are as follows:

-- raw_text: This is the reviews section (str)

-- aspectTerms: This is the set of aspect terms and their polarities to be present as a list of dictionaries. Each dictionary will have atleast two keys with the one of the key term and the value which is an aspect in the corresponding sentence. The second key will be polarity and its value is the polarity for corresponding aspect. ([{'term':'aspect1', 'polarity':'polarity1'}, ...])

Warning When creating the dataset in this fashion and saving it, .xlsx/.csv format will convert the aspectTerms column into string/text format. But the package will handle that when loading the dataset file.

An example dataset is shown below and also in the Datasets folder.

All the model weights can be found here. The best performing models for each ABSA subtask based on our experiments are presented in the table below:

The ATE models can be trained from scratch or alternatively can be used to run inference on your datasets directly. This can be done through CLI (check the Scripts folder) or by adapting your code similar to run_model.py. An example shell command to run inference on individual samples is shown below.

A sample notebook for training and evluating ATE can be found here.

To evaluate the ATE subtask on a single input using CLI run the following:

python run_model.py -mode cli -task ate \
-model_checkpoint kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined \
-test_input 'The cab ride was amazing but the service was pricey'

The ATSC models can be trained from scratch or alternatively can be used to run inference on your datasets directly. This can be done through CLI (check the Scripts folder) or by adapting your code similar to run_model.py. An example shell command to run inference on individual samples is shown below.

To evaluate the ATSC subtask on a single input using CLI run the following:

python run_model.py -mode cli -task atsc \
-model_checkpoint kevinscaria/atsc_tk-instruct-base-def-pos-neg-neut-combined \
-test_input 'The ambience was amazing but the waiter was rude|ambience'

Note the | delimiter that is used to pass the aspect term for which the polarity is to be extracted.

The Joint task models can be trained from scratch or alternatively can be used to run inference on your datasets directly. This can be done through CLI (check the Scripts folder) or by adapting your code similar to run_model.py. An example shell command to run inference on individual samples is shown below.

A sample notebook for training and evluating Joint Task can be found here.

To evaluate the Joint Task on a single input using CLI run the following:

python run_model.py -mode cli -task joint \
-model_checkpoint kevinscaria/joint_tk-instruct-base-def-pos-neg-neut-combined \
-test_input 'The cab ride was amazing but the service was pricey'

If you find our work useful, please cite our work:

article{scaria2023instructabsa,
  title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
  author={Scaria, Kevin and Gupta, Himanshu and Sawant, Saurabh Arjun and Mishra, Swaroop and Baral, Chitta},
  journal={arXiv preprint arXiv:2302.08624},
  year={2023}
}