We formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives.

この目的のために、我々は、入力が未知のノイズ処理を受ける可能性があるノイズの多いシーケンスラベリング問題を定式化し、摂動された入力に対して実行されるシーケンス ...

The noisy sequence labeling problem is formulated and two Noise-Aware Training objectives are proposed that improve robustness of sequence labeling ...

Sequence labeling systems should perform re- liably not only under ideal conditions but also with corrupted inputs—as these systems often.

This is an embedded video. Talk and the respective paper are published at ACL 2020 virtual conference. If you are one of the authors of the paper and want to ...

In information theory and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the ...

Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling ...

2021/05/09 · Taggers are expected to perform reliably not only under clean text but also real-world noisy text. This paper proposes two training ...

NAT aims to improve robustness of sequence labeling performed on data from noisy sources, like Optical Character Recognition (OCR), Automatic Speech ...

NAT is label-efficient – a NAT-trained model re- duces human-labeling ... Third, a NAT-trained model is robust in terms of extraction performance – it ...