mlsanityData to 7 Oct 2026

SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

arXiv 1708.00055 ↗First posted 31 Jul 2017cs.CLcited by 998

Not on the map. Why?

Abstract (arXiv)

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).

Key facts. Cited by 998 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 177; 2022: 230; 2023: 210; 2024: 179; 2025: 118; 2026: 84.

998 papers.

Sort:

By area:

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  3. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,234
  4. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
  5. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  6. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  7. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  8. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 718
  9. 2023Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunchcited by 679
  10. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  11. 2021Merging Models with Fisher-Weighted Averagingcited by 613
  12. 2022ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformerscited by 517
38 more
  1. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 481
  2. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
  3. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  4. 2021Compacter: Efficient Low-Rank Hypercomplex Adapter Layerscited by 458
  5. 2022Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Taskscited by 420
  6. 2022Dataless Knowledge Fusion by Merging Weights of Language Modelscited by 384
  7. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 329
  8. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  9. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  10. 2021I-BERT: Integer-only BERT Quantizationcited by 250
  11. 2023Pretraining Language Models with Human Preferencescited by 245
  12. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  13. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  14. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 233
  15. 2021ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfercited by 231
  16. 2022LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learningcited by 228
  17. 2021WARP: Word-level Adversarial ReProgrammingcited by 220
  18. 2022Extracting Latent Steering Vectors from Pretrained Language Modelscited by 217
  19. 2022LinkBERT: Pretraining Language Models with Document Linkscited by 209
  20. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  21. 2021Whitening Sentence Representations for Better Semantics and Faster Retrievalcited by 198
  22. 2023LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Modelscited by 195
  23. 2021Muppet: Massive Multi-task Representations with Pre-Finetuningcited by 192
  24. 2021Generating Datasets with Pretrained Language Modelscited by 192
  25. 2022Language model compression with weighted low-rank factorizationcited by 192
  26. 2022Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Modelscited by 179
  27. 2022Structured Pruning Learns Compact and Accurate Modelscited by 167
  28. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  29. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  30. 2022A Fast Post-Training Pruning Framework for Transformerscited by 161
  31. 2021Charformer: Fast Character Transformers via Gradient-based Subword Tokenizationcited by 158
  32. 2021Delving into Deep Imbalanced Regressioncited by 157
  33. 2023Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERTcited by 155
  34. 2023Composing Parameter-Efficient Modules with Arithmetic Operationscited by 155
  35. 2022Instruction Induction: From Few Examples to Natural Language Task Descriptionscited by 152
  36. 2022On the Effectiveness of Parameter-Efficient Fine-Tuningcited by 140
  37. 2021Learned Token Pruning for Transformerscited by 138
  38. 2022DiffCSE: Difference-based Contrastive Learning for Sentence Embeddingscited by 131

50 most cited of 998 All 998 (JSON)