SlowGuess/NAACL_2021_OCR huggingface dataset:
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When constructing support and query sets, we skip words that contain only one sense or words that have only one example per sense since they cannot be split into meaningful support and query sets (e.g., only two senses and one example each for a word). During inference, all the aforementioned examples are used as supports.
For BERT-classifier, all parameters are finetuned during supervised training. We use batch size of 4 sentences, and runs for 20 epochs. Our non-parametric model constructs an episode for each word type, accumulate gradients for 5 episodes before performing gradient update, runs for 100 epochs. All models use the AdamW optimizer with learning rate 1e-5.