antot / human_parity_mt Goto Github PK

This repository contains supplementary material for the publication:

Antonio Toral, Sheila Castilho, Ke Hu and Andy Way. 2018. Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation. WMT.

• export_appraise/ export of judgements from Appraise, and derived rankings, clusters and inter annotator agreement

  • export_appraise.sh script that runs all the steps
  • export/ judgements exported from Appraise in csv format for all the 49 documents
  • clusters/ resulting clusters
  • plot_cis.ods calculations and plot of Trueskill's confidence intervals
  • wmt-trueskill/ third-party code to calculate rankings and clusters
  • compute_agreement_scores.py third-party code to calculate IAA

• import_appraise/

  • xml/ 1 file in Appraise XML input format for each of the 169 documents from the WMT2017 data for the Chinese-English language pair. The order of the documents is randomised. Each document contains as metadata its original document identifier (e.g. sina0812.news.doc-ifxuxhas1768823) and the original language in which the document was written (en|zh)
  • plain/ 5 plain text files per document: source (sl), human translation (tl), Microsoft's output (c6) and Google's output (gg)

• regression/ R noteboook with a step-by-step regression analysis. We augment Microsoft's data with several additional predictors: sentence length, original language of the source text and document identifier.

• ttr/ type-token ratio calculations