mlsanityData to 14 Oct 2026

TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages

arXiv 2211.16496 ↗First posted 29 Nov 2022cs.CLcited by 21

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Abstract (arXiv)

We study politeness phenomena in nine typologically diverse languages. Politeness is an important facet of communication and is sometimes argued to be cultural-specific, yet existing computational linguistic study is limited to English. We create TyDiP, a dataset containing three-way politeness annotations for 500 examples in each language, totaling 4.5K examples. We evaluate how well multilingual models can identify politeness levels -- they show a fairly robust zero-shot transfer ability, yet fall short of estimated human accuracy significantly. We further study mapping the English politeness strategy lexicon into nine languages via automatic translation and lexicon induction, analyzing whether each strategy's impact stays consistent across languages. Lastly, we empirically study the complicated relationship between formality and politeness through transfer experiments. We hope our dataset will support various research questions and applications, from evaluating multilingual models to constructing polite multilingual agents.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 29 Nov 2022.

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24 of 41 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  5. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  6. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  7. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
  8. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 742
  9. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
  10. 2019Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTcited by 301
  11. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 243
  12. 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 211
12 more
  1. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  2. 2020MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answeringcited by 153
  3. 2019CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairscited by 136
  4. 2013A Computational Approach to Politeness with Application to Social Factorscited by 106
  5. 2021Language Models are Few-shot Multilingual Learnerscited by 92
  6. 2018Polite Dialogue Generation Without Parallel Datacited by 59
  7. 2021XFORMAL: A Benchmark for Multilingual Formality Style Transfercited by 52
  8. 2021Discrete and Soft Prompting for Multilingual Modelscited by 49
  9. 2021MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translatorscited by 35
  10. 2016Interpreting Neural Networks to Improve Politeness Comprehensioncited by 12
  11. 2015SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicaturecited by 11
  12. 2020Facilitating the Communication of Politeness through Fine-Grained Paraphrasingcited by 9

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Citing papers per year: 2023: 4; 2024: 6; 2025: 6; 2026: 5.

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  1. 2023When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Datasetcited by 52
  2. 2024Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Artcited by 26
  3. 2024Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialoguecited by 16
  4. 2024Computational Politeness in Natural Language Processing: A Surveycited by 8
  5. 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queriescited by 6
  6. 2025Towards Style Alignment in Cross-Cultural Translationcited by 4
  7. 2024What is "Typological Diversity" in NLP?cited by 3
  8. 2025Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformationcited by 3
  9. 2026Answer Bubbles: Information Exposure in AI-Mediated Searchcited by 3
  10. 2023Comparing Styles across Languages: A Cross-Cultural Exploration of Politenesscited by 2
  11. 2023Multilingual estimation of political-party positioning: From label aggregation to long-input Transformerscited by 2
  12. 2025Does AI Coaching Prepare us for Workplace Negotiations?cited by 2
9 more
  1. 2026Not My Truce: Personality Differences in AI-Mediated Workplace Negotiationcited by 2
  2. 2023Politeness Stereotypes and Attack Vectors: Gender Stereotypes in Japanese and Korean Language Modelscited by 1
  3. 2025Does the Appearance of Autonomous Conversational Robots Affect User Spoken Behaviors in Real-World Conference Interactions?cited by 1
  4. 2024Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferencescited by 0
  5. 2024"My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguisecited by 0
  6. 2025Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profilescited by 0
  7. 2026ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmaticscited by 0
  8. 2026A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settingscited by 0
  9. 2026CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systemscited by 0