Position-Aware-Tagging-for-ASTE
[EMNLP 2020] Position-Aware Tagging for Aspect Sentiment Triplet Extraction (In EMNLP 2020)
Evaluation on ASOTE
We folk this repository from the Position-Aware-Tagging-for-ASTE repository and evaluate the Position-Aware-Tagging model on the ASOTE task.
Supported datasets include 14res, 14lap, 15res, 16res.
Running with GloVe (M=6)
sh run.sh jet_t.py --base_dir JET/T/ --dataset 14res
sh run.sh jet_o.py --base_dir JET/O/ --dataset 14res
Note that, when M=6, run on the 14res and 14lap datasets, both jet_t and jet_o need 60G memory; run on the 15res and 16res datasets, both jet_t and jet_o need 40G memory.
Running with BERT (M=6)
sh run_bert.sh jet_t.py --base_dir JET/BERT/T/ --dataset 14res
sh run_bert.sh jet_o.py --base_dir JET/BERT/O/ --dataset 14res
Note that, when M=6, run on the 14res and 14lap datasets, both jet_t and jet_o need 62G memory; run on the 15res and 16res datasets, both jet_t and jet_o need 40G memory.
Task Description
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. This task is firstly proposed by (Peng et al., 2020) in the paper publised in AAAI 2020, Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (In AAAI 2020)
For Example:
Given the sentence:
The screen is very large and crystal clear with amazing colors and resolution .
The objective of the Aspect Sentiment Triplet Extraction (ASTE) task is to predict the triplets:
[('screen', 'large', 'Positive'), ('screen', 'clear', 'Positive'), ('colors', 'amazing', 'Positive'), ('resolution', 'amazing', 'Positive')]
where a triplet consists of (target, opinion, sentiment).
Requirement
conda create -n JET python=3.7 anaconda
conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch
Pytorch 1.4
Python 3.7.3
Transformers
Bert-as-service
Running with GloVe
python jet_o.py
By default, the model runs on 2014 laptop dataset with provided hyper-parameters (M=2) without BERT. Change line 20-27 for different datasets.
python jet_t.py
By default, the model runs on 2015 reataurant dataset with provided hyper-parameters (M=2) without BERT. Change line 20-27 for different datasets.
Running with BERT
Please install bert-as-service before Start the BERT service:
bert-serving-start -pooling_layer -1 -model_dir uncased_L-12_H-768_A-12 -max_seq_len=NONE -num_worker=2 -port=8880 -pooling_strategy=NONE -cpu -show_tokens_to_client
Then,
python jet_o.py
Change line 27 in the current file to True to runs on 2014 laptop dataset with provided hyper-parameters (M=2) with BERT. Change line 20-27 for different datasets.
python jet_t.py
Change line 27 in the current file to True to runs on 2015 reataurant dataset with provided hyper-parameters (M=2) with BERT. Change line 20-27 for different datasets.
Task Lists
- The current framwork only support BATCH_SIZE=1, more work need to be done to support batch calculation.
Related Repo
The code are created based on the StatNLP framework.