xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark Article Swipe
Chen Zhang
,
Luis Fernando D’Haro
,
Chengguang Tang
,
Ke Shi
,
Guohua Tang
,
Haizhou Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.08958
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.08958
Recent advancements in reference-free learned metrics for open-domain dialogue evaluation have been driven by the progress in pre-trained language models and the availability of dialogue data with high-quality human annotations. However, current studies predominantly concentrate on English dialogues, and the generalization of these metrics to other languages has not been fully examined. This is largely due to the absence of a multilingual dialogue evaluation benchmark. To address the issue, we introduce xDial-Eval, built on top of open-source English dialogue evaluation datasets. xDial-Eval includes 12 turn-level and 6 dialogue-level English datasets, comprising 14930 annotated turns and 8691 annotated dialogues respectively. The English dialogue data are extended to nine other languages with commercial machine translation systems. On xDial-Eval, we conduct comprehensive analyses of previous BERT-based metrics and the recently-emerged large language models. Lastly, we establish strong self-supervised and multilingual baselines. In terms of average Pearson correlations over all datasets and languages, the best baseline outperforms OpenAI's ChatGPT by absolute improvements of 6.5% and 4.6% at the turn and dialogue levels respectively, albeit with much fewer parameters. The data and code are publicly available at https://github.com/e0397123/xDial-Eval.
Related Topics
Concepts
Computer science
Benchmark (surveying)
Natural language processing
Generalization
Baseline (sea)
Machine translation
Artificial intelligence
Open domain
Code (set theory)
Domain (mathematical analysis)
Quality (philosophy)
Programming language
Question answering
Set (abstract data type)
Geology
Geodesy
Epistemology
Mathematics
Philosophy
Mathematical analysis
Oceanography
Geography
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.08958
- https://arxiv.org/pdf/2310.08958
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387687500
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4387687500Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.08958Digital Object Identifier
- Title
-
xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation BenchmarkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-13Full publication date if available
- Authors
-
Chen Zhang, Luis Fernando D’Haro, Chengguang Tang, Ke Shi, Guohua Tang, Haizhou LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.08958Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.08958Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.08958Direct OA link when available
- Concepts
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Computer science, Benchmark (surveying), Natural language processing, Generalization, Baseline (sea), Machine translation, Artificial intelligence, Open domain, Code (set theory), Domain (mathematical analysis), Quality (philosophy), Programming language, Question answering, Set (abstract data type), Geology, Geodesy, Epistemology, Mathematics, Philosophy, Mathematical analysis, Oceanography, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fewer | 172 |
| abstract_inverted_index.fully | 50 |
| abstract_inverted_index.human | 28 |
| abstract_inverted_index.large | 127 |
| abstract_inverted_index.other | 45, 107 |
| abstract_inverted_index.terms | 139 |
| abstract_inverted_index.these | 42 |
| abstract_inverted_index.turns | 93 |
| abstract_inverted_index.Recent | 0 |
| abstract_inverted_index.albeit | 169 |
| abstract_inverted_index.driven | 12 |
| abstract_inverted_index.issue, | 68 |
| abstract_inverted_index.levels | 167 |
| abstract_inverted_index.models | 19 |
| abstract_inverted_index.strong | 133 |
| abstract_inverted_index.ChatGPT | 154 |
| abstract_inverted_index.English | 36, 77, 88, 100 |
| abstract_inverted_index.Lastly, | 130 |
| abstract_inverted_index.Pearson | 142 |
| abstract_inverted_index.absence | 58 |
| abstract_inverted_index.address | 66 |
| abstract_inverted_index.average | 141 |
| abstract_inverted_index.conduct | 117 |
| abstract_inverted_index.current | 31 |
| abstract_inverted_index.largely | 54 |
| abstract_inverted_index.learned | 4 |
| abstract_inverted_index.machine | 111 |
| abstract_inverted_index.metrics | 5, 43, 123 |
| abstract_inverted_index.models. | 129 |
| abstract_inverted_index.studies | 32 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.OpenAI's | 153 |
| abstract_inverted_index.absolute | 156 |
| abstract_inverted_index.analyses | 119 |
| abstract_inverted_index.baseline | 151 |
| abstract_inverted_index.datasets | 146 |
| abstract_inverted_index.dialogue | 8, 24, 62, 78, 101, 166 |
| abstract_inverted_index.extended | 104 |
| abstract_inverted_index.includes | 82 |
| abstract_inverted_index.language | 18, 128 |
| abstract_inverted_index.previous | 121 |
| abstract_inverted_index.progress | 15 |
| abstract_inverted_index.publicly | 179 |
| abstract_inverted_index.systems. | 113 |
| abstract_inverted_index.annotated | 92, 96 |
| abstract_inverted_index.available | 180 |
| abstract_inverted_index.datasets, | 89 |
| abstract_inverted_index.datasets. | 80 |
| abstract_inverted_index.dialogues | 97 |
| abstract_inverted_index.establish | 132 |
| abstract_inverted_index.examined. | 51 |
| abstract_inverted_index.introduce | 70 |
| abstract_inverted_index.languages | 46, 108 |
| abstract_inverted_index.BERT-based | 122 |
| abstract_inverted_index.baselines. | 137 |
| abstract_inverted_index.benchmark. | 64 |
| abstract_inverted_index.commercial | 110 |
| abstract_inverted_index.comprising | 90 |
| abstract_inverted_index.dialogues, | 37 |
| abstract_inverted_index.evaluation | 9, 63, 79 |
| abstract_inverted_index.languages, | 148 |
| abstract_inverted_index.turn-level | 84 |
| abstract_inverted_index.xDial-Eval | 81 |
| abstract_inverted_index.concentrate | 34 |
| abstract_inverted_index.open-domain | 7 |
| abstract_inverted_index.open-source | 76 |
| abstract_inverted_index.outperforms | 152 |
| abstract_inverted_index.parameters. | 173 |
| abstract_inverted_index.pre-trained | 17 |
| abstract_inverted_index.translation | 112 |
| abstract_inverted_index.xDial-Eval, | 71, 115 |
| abstract_inverted_index.advancements | 1 |
| abstract_inverted_index.annotations. | 29 |
| abstract_inverted_index.availability | 22 |
| abstract_inverted_index.correlations | 143 |
| abstract_inverted_index.high-quality | 27 |
| abstract_inverted_index.improvements | 157 |
| abstract_inverted_index.multilingual | 61, 136 |
| abstract_inverted_index.comprehensive | 118 |
| abstract_inverted_index.predominantly | 33 |
| abstract_inverted_index.respectively, | 168 |
| abstract_inverted_index.respectively. | 98 |
| abstract_inverted_index.dialogue-level | 87 |
| abstract_inverted_index.generalization | 40 |
| abstract_inverted_index.reference-free | 3 |
| abstract_inverted_index.self-supervised | 134 |
| abstract_inverted_index.recently-emerged | 126 |
| abstract_inverted_index.https://github.com/e0397123/xDial-Eval. | 182 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |