While automatically computing numerical scores remains the dominant paradigm in NLP system evaluation, error annotation and analysis is receiving increasing attention, with several error annotation schemes recently proposed for automatically generated text. However, there is little agreement about what error annotation schemes should look like, how many different types of errors should be distinguished and at what level of granularity. In this paper, our aim is to map out work on annotating errors in human and machine generated text, with a particular focus on error taxonomies. We describe our paper selection process, and survey the error annotation schemes reported in the papers, drawing out similarities and differences between them. Finally, we characterise the issues that would make it difficult to move from the current situation to a standardised error taxonomy for annotating errors in automatically generated text.

In this paper, we introduce a sentence-level comparable text corpus crawled and created for the less-resourced language pair, Manipuri (mni) and English (eng). Our monolingual corpora comprise 1.88 million Manipuri sentences and 1.45 million English sentences, and our parallel corpus comprises 124,975 Manipuri-English sentence pairs. These data were crawled and collected over a year from August 2020 to March 2021 from a local newspaper website called 'The Sangai Express.' The resources reported in this paper are made available to help the low-resourced languages community for MT/NLP tasks 1 .