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A deep Transformer model with good evaluation score does not mean eachsubnetwork (a.k.a transformer block) learns reasonable representation.Diagnosing abnormal representation and avoiding it can contribute to achievinga better evaluation score. We propose an innovative perspective for analyzingattention patterns: summarize block-level patterns and assume abnormal patternscontribute negative influence. We leverage Wav2Vec 2.0 as a research target andanalyze a pre-trained model's pattern. All experiments leverageLibrispeech-100-clean as training data. Through avoiding diagnosed abnormalones, our custom Wav2Vec 2.0 outperforms the original version about 4.8%absolute word error rate (WER) on test-clean with viterbi decoding. Our versionis still 0.9% better when decoding with a 4-gram language model. Moreover, weidentify that avoiding abnormal patterns is the main contributor forperformance boosting.

Interpreting A Pre-trained Model Is A Key For Model Architecture Optimization: A Case Study On Wav2Vec 2.0