Piotr Szymański


2020

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WER we are and WER we think we are
Piotr Szymański | Piotr Żelasko | Mikolaj Morzy | Adrian Szymczak | Marzena Żyła-Hoppe | Joanna Banaszczak | Lukasz Augustyniak | Jan Mizgajski | Yishay Carmiel
Findings of the Association for Computational Linguistics: EMNLP 2020

Natural language processing of conversational speech requires the availability of high-quality transcripts. In this paper, we express our skepticism towards the recent reports of very low Word Error Rates (WERs) achieved by modern Automatic Speech Recognition (ASR) systems on benchmark datasets. We outline several problems with popular benchmarks and compare three state-of-the-art commercial ASR systems on an internal dataset of real-life spontaneous human conversations and HUB’05 public benchmark. We show that WERs are significantly higher than the best reported results. We formulate a set of guidelines which may aid in the creation of real-life, multi-domain datasets with high quality annotations for training and testing of robust ASR systems.

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Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing
Piotr Szymański | Kyle Gorman
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Recent work raises concerns about the use of standard splits to compare natural language processing models. We propose a Bayesian statistical model comparison technique which uses k-fold cross-validation across multiple data sets to estimate the likelihood that one model will outperform the other, or that the two will produce practically equivalent results. We use this technique to rank six English part-of-speech taggers across two data sets and three evaluation metrics.