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This repository contains the WordNet Language Model Probing (WNLaMPro) dataset introduced in "Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking".

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WordNet Language Model Probing

This repository contains the WordNet Language Model Probing (WNLaMPro) dataset. Each line of the dataset file (dataset/WNLaMPro.txt) has the following form (note that all columns are separated by tabs rather than spaces):

<ID>  <SET_TYPE>  <KEY_WORD>  <RELATION>  <TARGET_WORD1>  <TARGET_WORD2>  ...

The columns have the following meaning:

  • <ID>: A unique identifier for this dataset entry
  • <SET_TYPE>: Either test or dev, depending on whether this entry belongs to the development or test subset of PSR
  • <KEY_WORD>: The key word in the <ANNOTATED_WORD> format (see below)
  • <RELATION>: The relation of this entry, either antonym, hypernym, cohyponym or corruption
  • <TARGET_WORDn>: The n-th target word for this dataset entry, in the <ANNOTATED_WORD> format (see below)

Annotated Words

Each key and target word of the WNLaMPro dataset is represented as an <ANNOTATED_WORD> in the following form:

<ANNOTATED_WORD> := <WORD> (<POS>,<FREQ>,<COUNT>)

The columns have the following meaning:

  • <WORD>: The actual word
  • <POS>: The part-of-speech tag for this word (either noun or adjective)
  • <FREQ>: The estimated Zipf frequency for this word, obtained using wordfreq
  • <COUNT>: The number of occurrences of this word in the Westbury Wikipedia corpus

Evaluation Script

You can evaluate a pretrained language model on WNLaMPro as follows:

python3 eval-script/evaluate.py --root ROOT --predictions_file PREDICTIONS_FILE --output_file OUTPUT_FILE --model_cls MODEL_CLS --model_name MODEL_NAME (--embeddings EMBEDDINGS)

where

  • ROOT is the path to the directory where WNLaMPro.txt can be found;
  • PREDICTIONS_FILE is the name of the file in which predictions are to be stored (relative to ROOT);
  • OUTPUT_FILE is the name of the file in which the model's MRR is to be stored (relative to ROOT);
  • MODEL_CLS is either bert or roberta (the evaluation script currently does not support other pretrained language models);
  • MODEL_NAME is either the name of a pretrained model from the Hugging Face Transformers Library (e.g., bert-base-uncased) or the path to a finetuned model;
  • EMBEDDINGS (optional) is the path (relative to ROOT) of a file that contains embeddings which are used to overwrite the language model's original embeddings. Each line of this file has to be in the format <WORD> <EMBEDDING>, for example apple -0.12 3.45 0.23 ... 0.03.

For additional parameters, check the content of eval-script/evaluate.py or run python3 eval-script/evaluate.py --help.

Citation

If you make use of the WNLaMPro dataset, please cite the following paper:

@inproceedings{schick2020rare,
  title={Rare words: A major problem for contextualized representation and how to fix it by attentive mimicking},
  author={Schick, Timo and Sch{\"u}tze, Hinrich},
  url="https://arxiv.org/abs/1904.06707",
  booktitle={Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence},
  year={2020}
}

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This repository contains the WordNet Language Model Probing (WNLaMPro) dataset introduced in "Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking".

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