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@inproceedings{moeller23:_attrib_method_siames_encod,
  abstract = {Despite the success of Siamese encoder models such as sentence
transformers (ST), little is known about the aspects of inputs they
pay attention to.  A barrier is that their predictions cannot be
attributed to individual features, as they compare two inputs rather
than processing a single one.
This paper derives a local attribution method for Siamese encoders by generalizing
the principle of integrated gradients to models with multiple inputs.
The solution takes the form of feature-pair attributions, and can be reduced to a token-token matrix for STs.
Our method involves the introduction of integrated Jacobians and inherits the advantageous formal properties of integrated gradients: it accounts for the model's full computation graph and is guaranteed to converge to the actual prediction.
A pilot study shows that in an ST few token-pairs can often explain large fractions of predictions, and it focuses on nouns and verbs.
For accurate predictions, it however needs to attend to the majority of tokens and parts of speech.
},
  added-at = {2023-10-07T22:28:13.000+0200},
  address = {Singapore},
  author = {M{ö}ller, Lucas and Nikolaev, Dmitry and Pad{ó}, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/287b7c46571480164f9d4d94074a7467a/sp},
  booktitle = {Proceedings of EMNLP},
  interhash = {3d5cea892bbb78bdf925f5eae5aeb211},
  intrahash = {87b7c46571480164f9d4d94074a7467a},
  keywords = {conference myown},
  note = {To appear},
  timestamp = {2023-10-19T15:57:48.000+0200},
  title = {An Attribution Method for Siamese Encoders},
  url = {https://arxiv.org/pdf/2310.05703.pdf},
  year = 2023
}

@InProceedings{moeller22:understanding,
  author =       {Lucas M{ö}ller and Sebastian Padó},
  title =        {Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation},
  booktitle = {Proceedings of the SIGIR Workshop on News Recommendation and Analytics},
  year =         2022,
  keywords = {myown workshop},
}

@article{moeller24:explaining-neural,
  author =       {Lucas Möller and Sebastian Padó},
  title =        {Explaining Neural News Recommendation with Attributions onto Reading Histories},
  journal =      {ACM Transactions on Intelligent Systems and Technology},
  url = {https://doi.org/10.1145/3673233},
  year = 2024}