mlsanityData to 14 Oct 2026

I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews

arXiv 2104.06893 ↗First posted 14 Apr 2021cs.CL cs.AI cs.LGcited by 45

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Abstract (arXiv)

Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). Builds on 19 of its 56 references that are in the map. First posted on arXiv 14 Apr 2021.

Builds on

19 of 56 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  6. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
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  9. 2017Counterfactual Fairnesscited by 804
  10. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 439
  11. 2017Breaking the Softmax Bottleneck: A High-Rank RNN Language Modelcited by 152
  12. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
7 more
  1. 2018Challenges of Using Text Classifiers for Causal Inferencecited by 33
  2. 2020SemEval-2020 Task 5: Counterfactual Recognitioncited by 12
  3. 2017Think Globally, Embed Locally --- Locally Linear Meta-embedding of Wordscited by 8
  4. 2020BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation modelscited by 3
  5. 2020Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Predictioncited by 1
  6. 2020IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactualscited by 1
  7. 2020ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modelingcited by 1

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Citing papers per year: 2022: 2; 2023: 6; 2024: 9; 2025: 14; 2026: 14.

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  1. 2022MTEB: Massive Text Embedding Benchmarkcited by 640
  2. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 472
  3. 2022Efficient Few-Shot Learning Without Promptscited by 137
  4. 2024Making Text Embedders Few-Shot Learnerscited by 79
  5. 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledgecited by 51
  6. 2025Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Taskscited by 51
  7. 2025KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Modelcited by 36
  8. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  9. 2024Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystackcited by 19
  10. 2026F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual Worldcited by 12
  11. 2025Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddingscited by 11
  12. 2023Automated Few-shot Classification with Instruction-Finetuned Language Modelscited by 9
33 more
  1. 2025Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Taskscited by 8
  2. 2025F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Datacited by 8
  3. 2025Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformationcited by 7
  4. 2025Enhancing Lexicon-Based Text Embeddings with Large Language Modelscited by 6
  5. 2026CSRv2: Unlocking Ultra-Sparse Embeddingscited by 6
  6. 2024Bilingual BSARD: Extending Statutory Article Retrieval to Dutchcited by 5
  7. 2025New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Modelscited by 5
  8. 2026The Embedder's Dilemma: LLMs Are Better, but at What Cost?cited by 5
  9. 2024DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learningcited by 4
  10. 2025Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddingscited by 4
  11. 2023AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classificationcited by 3
  12. 2025LGAI-EMBEDDING-Preview Technical Reportcited by 3
  13. 2025On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Surveycited by 3
  14. 2025PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMscited by 3
  15. 2026One prompt is not enough: Instruction Sensitivity Undermines Embedding Model Evaluationcited by 3
  16. 2026Uncovering the Latent Potential of Deep Intermediate Representationscited by 3
  17. 2023Non-contrastive sentence representations via self-supervisioncited by 2
  18. 2023Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Modelscited by 2
  19. 2026Zero-Shot Quantization via Weight-Space Arithmeticcited by 2
  20. 2024Topic-aware Causal Intervention for Counterfactual Detectioncited by 1
  21. 2024Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classificationcited by 1
  22. 2025Pruning General Large Language Models into Customized Expert Modelscited by 1
  23. 2026Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Modelscited by 1
  24. 2026Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMscited by 1
  25. 2026The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustnesscited by 1
  26. 2023Enhancing Sentiment Analysis Results through Outlier Detection Optimizationcited by 0
  27. 2024Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learningcited by 0
  28. 2025Refract ICL: Rethinking Example Selection in the Era of Million-Token Modelscited by 0
  29. 2026ML-Embed: Inclusive and Efficient Embeddings for a Multilingual Worldcited by 0
  30. 2026To MRL or not to MRL: Text Embeddings are Robust to Truncation Without Matryoshka Learning, Except In Heavy Truncation Scenarioscited by 0
  31. 2026BitNet Text Embeddingscited by 0
  32. 2026The Undetected Damage of Quantization on Retrieval and How to Fix Itcited by 0
  33. 2026Towards Universal Calibration for In-Context Classificationcited by 0