ASIV

Implementation of ASIV described in our research work Asymmetric feature interaction for interpreting model predictions.

Studying word interaction could help identify to what extent a set of words exert influence in combination as opposed to independently. However, most interaction attribution methods assume symmetric interaction, which may fail to capture asymmetric influence that contributes to model prediction. For example, in individual level explanation “funny” has negative influence while the symmetric interaction between “funny" and “not" produces positive influence to model prediction. Therefore the influence of the presence of “not” to “funny” is not the same as that of the presence of “funny” to “not".

This work is the first step toward providing the explanation that incorporates asymmetric feature interaction, and our research aims to abstract complex feature interactions in deep NLP models.

Fig.1. Explanations for a negative movie review (computed by Shapley value and Shapley interaction index), where the color indicates contribution of the corresponding word/pairwise word interaction to the model prediction.

Fig.2. Symmetric versus asymmetric pairwise interaction (computed by our method) where the directed edge $a\rightarrow b$ refers to in the presence of $a$ how much contribution of $b$ made to the model prediction. The presence of "very" does not influence "funny" much while "funny" further modifies "very" and thus the interaction influence of "funny" $\rightarrow$ "very" is stronger than that of "very" $\rightarrow$ "funny". }

Examples

• Basic configuration: pytorch == 1.12.1, python == 3.8.15, numpy == 1.24.0

• Src: we present the example code of ASIV to interpret SST-2 sentiment analyisis over BERT architecture. It is flexible to custermize the data, architecture and computing details.

  • Train NLP model

    python training_model.py
    

  • ASIV algorithm: asiv.py
  • Run ASIV to generate interaction explanation

    python compute_asiv.py
    

    (please download the pretrained domain-specific language model and specify the path)

• A hypergraph structure could be pre-defined and use ASIV to compute the weight of hyperedge.

Our pretrained LM (BERT + RoBERTa)

SST / Yelp2 (The pretrained LM could be improved and you could customize pretrain section)