ExplainTI, a framework of explaining table interpretation using multi-view explanations. ExplainTI consists of two phases: tables are converted to sequences and lightweight column graphs firstly, then a pre-trained transformer encoder is fine-tuned to aggregate contextual information and provide multi-view explanations: (i) Local Explanations, are the most relevant phrases (w.r.t. pairwise phrases) in columns (w.r.t. column pairs) for the predictions; (ii) Global Explanations are the most influential samples from training data, which tend to have similar labels to the input sample; (iii) Structural Explanations, we design a relatively small-scale but highly effective method to construct column graphs for tables, which can not only aggregate contextual information but also provide explanations from structural view via graph attention mechanism.
- Python 3.7.11
- PyTorch 1.9.0+cu111
- HuggingFace Transformers 4.11.3
- faiss 1.7.1
- scikit-learn 1.0
We used two real world large-scale benchmark datasets from different types for evaluation.
WikiTable
The WikiTable dataset is Web tables collected from Wikipedia. We used the same train/valid/test splits as TURL.
GitTable
The GitTable dataset is the first large-scale relational table corpus. We used its subset organism and split it into train, valid, and test using the radio of 8:1:1.
The raw dataset is from GitTable. The code of preprocessing is in gittable.py and the preprocessed data is in data/GitTable.
python build_graph.py [<args>] [-h | --help]
e.g.
python build_graph.py --data_name=GitTable --sample_size=16
To train the model and get the explanations with ExplainTI:
python train.py [<args>] [-h | --help]
e.g.
python train.py --le=0.1 --ge=0.1 --se --save_model --data_name=GitTable --path=./data/GitTable --batch_size=160 --column_length=64 --num_type=1141
The meaning of the flags:
--data_name: the name of the dataset. e.g.WikiTable--path: the local path of the dataset. e.g../data/WikiTable--column_length: maximum length of a column after being converted to a sequence. e.g.64--n_epoch: the number of epochs. e.g.40--lr: the learning rate. e.g.5e-5--model: the language model to be fine-tuned. e.g.bert-base-uncased--use_large: whether use large version of the model--le: the loss weight of the local view. e.g.0.05--ge: the loss weight of the global view. e.g.0.05--se: a flag indicating whether turn on the structural view--window_size: the number of tokens in a window. e.g.8--top_k: the number of explanations generated for each view. e.g.10--num_type: the number of types. e.g.255--num_relation: the number of relations. e.g.121--attention_method: the aggregate method for graph attention. e.g.dot--save_model: if this flag is on, then save the checkpoint to{save_path}/{name}.pt.--save_path: see--save_model. e.g../checkpoint--update_epoch: indicates how often the global embeddings store is updated. e.g.10--cell_duplicated: performs cell-level deduplication or not.