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Evaluating Automatic Metrics with Incremental Machine Translation Systems

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Guojun Wu, Shay B. Cohen, Rico Sennrich

Computation and Language · Submitted 3 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Overall, our research demonstrates the dataset's value as a testbed for metric evaluation.

how it is stated · asserted 1.00 read the record →

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arXiv ID2407.03277 (version 2)
Pith ID2PILRT2G, Pith's permanent record of this paper and its review
CitationGuojun Wu, Rico Sennrich, Shay B. Cohen. "Evaluating Automatic Metrics with Incremental Machine Translation Systems." arXiv:2407.03277 (2024). https://pith.science/paper/2PILRT2G
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,219 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.16 · human participants 0.19 · controlled comparison 0.19 · randomized assignment 0.02 · states quantitative result 0.46 · compares with prior results 0.92 · mentions code or data availability 0.98.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We introduce a dataset comprising commercial machine translations, gathered weekly over six years across 12 translation directions. Since human A/B testing is commonly used, we assume commercial systems improve over time, which enables us to evaluate machine translation (MT) metrics based on their preference for more recent translations. Our study not only confirms several prior findings, such as the advantage of neural metrics over non-neural ones, but also explores the debated issue of how MT quality affects metric reliability--an investigation that smaller datasets in previous research could not sufficiently explore. Overall, our research demonstrates the dataset's value as a testbed for metric evaluation. We release our code at https://github.com/gjwubyron/Evo

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We introduce a dataset comprising commercial machine translations, gathered weekly over six years across 12 translation directions. contribution 0.60 0.95 0.03 asserted 1.00
s1 Since human A/B testing is commonly used, we assume commercial systems improve over time, which enables us to evaluate machine translation (MT) metrics based on their preference for more recent translations. method 0.85 0.22 0.07 asserted 0.61
s2 Our study not only confirms several prior findings, such as the advantage of neural metrics over non-neural ones, but also explores the debated issue of how MT quality affects metric reliability--an investigation that smaller datasets in previous research could not sufficiently explore. contribution 0.57 0.87 0.18 asserted 0.98
s3 Overall, our research demonstrates the dataset's value as a testbed for metric evaluation. interpretation 0.56 0.88 0.75 asserted 1.00
s4 We release our code at https://github.com/gjwubyron/Evo contribution 0.96 0.85 0.04 asserted 0.79

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.65.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We introduce a dataset comprising commercial machine translations, gathered weekly over six years across 12 translation directions. Since human A/B testing is commonly used, we assume commercial systems improve over time, which enables us to evaluate machine translation (MT) metrics based on their preference for more recent translations. Our study not only confirms several prior findings, such as the advantage of neural metrics over non-neural ones, but also explores the debated issue of how MT quality affects metric reliability--an investigation that smaller datasets in previous research could not sufficiently explore. Overall, our research demonstrates the dataset's value as a testbed for metric evaluation. We release our code at https://github.com/gjwubyron/Evo

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