{
  "id": "2106.00143",
  "version": "v1",
  "published": "2021-05-31T23:21:10.000Z",
  "updated": "2021-05-31T23:21:10.000Z",
  "title": "An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers",
  "authors": [
    "Tharindu Ranasinghe",
    "Constantin Orasan",
    "Ruslan Mitkov"
  ],
  "comment": "Accepted to appear at the ACL-IJCNLP 2021 Main conference",
  "categories": [
    "cs.CL",
    "cs.AI",
    "cs.LG"
  ],
  "abstract": "Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that these QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-05-31T23:21:10.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multilingual word-level quality estimation",
      "exploratory analysis",
      "cross-lingual transformers",
      "language pair",
      "language-specific models"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}