mlsanityData to 7 Oct 2026

The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations

arXiv 1707.08172 ↗First posted 25 Jul 2017cs.CLcited by 23

Not on the map. Why?

Abstract (arXiv)

This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al.. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 12; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

23 papers.

Sort:

By area:

  1. 2022Instruction Induction: From Few Examples to Natural Language Task Descriptionscited by 152
  2. 2021Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinesecited by 45
  3. 2021Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrificecited by 30
  4. 2023Evaluating and Modeling Attribution for Cross-Lingual Question Answeringcited by 21
  5. 2021Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Modelscited by 18
  6. 2022BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examplescited by 15
  7. 2022Learning Better Masking for Better Language Model Pre-trainingcited by 14
  8. 2022Task Compass: Scaling Multi-task Pre-training with Task Prefixcited by 14
  9. 2023Universal Multimodal Representation for Language Understandingcited by 14
  10. 2022Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embeddingcited by 11
  11. 2022TRUE: Re-evaluating Factual Consistency Evaluationcited by 7
  12. 2022Rethinking Textual Adversarial Defense for Pre-trained Language Modelscited by 5
11 more
  1. 2024When LLMs Learn to be Students: The SOEI Framework for Modeling and Evaluating Virtual Student Agents in Educational Interactioncited by 4
  2. 2022Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Modelcited by 3
  3. 2025On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Surveycited by 3
  4. 2022Instance Regularization for Discriminative Language Model Pre-trainingcited by 2
  5. 2021Span Fine-tuning for Pre-trained Language Modelscited by 1
  6. 2022Compliance Checking with NLI: Privacy Policies vs. Regulationscited by 1
  7. 2022Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problemcited by 1
  8. 2022Language Model Pre-training on True Negativescited by 1
  9. 2024Ensembling Finetuned Language Models for Text Classificationcited by 1
  10. 2021Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentencescited by 0
  11. 2026CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocationcited by 0