ItsIRL

Intermediate Entity-based Sparse Interpretable Representation Learning

Intermediate Entity-based Sparse Interpretable Representation Learning
Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh, Byron Wallace
Blackbox NLP workshop at EMNLP 2022

• Paper: [ https://aclanthology.org/2022.blackboxnlp-1.17/ ]
• Poster: [ pdf ]
• Code: [ github ]

Abstract
Interpretable entity representations (IERs) are sparse embeddings that are “human-readable” in that dimensions correspond to fine-grained entity types and values are predicted probabilities that a given entity is of the corresponding type. These methods perform well in zero-shot and low supervision settings. Compared to standard dense neural embeddings, such interpretable representations may permit analysis and debugging. However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training. Can we maintain the interpretable semantics afforded by IERs while improving predictive performance on downstream tasks? T oward this end, we propose Intermediate enTity-based Sparse Interpretable Representation Learning (ItsIRL). ItsIRL realizes improved performance over prior IERs on biomedical tasks, while maintaining “interpretability” generally and their ability to support model debugging specifically. The latter is enabled in part by the ability to perform “counterfactual” fine-grained entity type manipulation, which we explore in this work. Finally, we propose a method to construct entity type based class prototypes for revealing global semantic properties of classes learned by our model.

How to pretrain ItsIRL and reproduce experiments

1. See ier_model/README.md for how to pretrain ItsIRL model
2. See experiments/README.md for how to run pretrained ItsIRL model on two tasks in paper ( ELC and BIOSSES )
3. See notebooks/ for the 3 notebooks related to:
   - Counterfactual Entity Type manipulation and learning Global Prototypes over ELC data
   - Entity Type Sparsity analysis for BIOSSES data
   - Baseline numbers for BIOSSES tasks ( Baselines for ELC found in BIERs repo )

Notebooks contained saved model checkpoints both for pre-trained ItsIRL and task specific fine-tuned models.

ier_model/ code adapted from [ BIERs github ]

@inproceedings{garcia-olano-2022-itsirl,
    title = "Intermediate Entity-based Sparse Interpretable Representation Learning",
    author = "Garcia-Olano, Diego  and
      Onoe, Yasumasa and
      Ghosh, Joydeep and
      Wallace, Byron",
    booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
}