KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding

Natural language inference (NLI) and semantic textual similarity (STS) are key tasks in natural language understanding (NLU). Although several benchmark datasets for those tasks have been released in English and a few other languages, there are no publicly available NLI or STS datasets in the Korean language. Motivated by this, we construct and release new datasets for Korean NLI and STS, dubbed KorNLI and KorSTS, respectively. Following previous approaches, we machine-translate existing English training sets and manually translate development and test sets into Korean. To accelerate research on Korean NLU, we also establish baselines on KorNLI and KorSTS. Our datasets are made publicly available via our GitHub repository.

[1]  Taku Kudo,et al.  SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing , 2018, EMNLP.

[2]  Omer Levy,et al.  SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.

[3]  Hugo Gonçalo Oliveira,et al.  Organizing the ASSIN 2 Shared Task , 2019, ASSIN@STIL.

[4]  Rico Sennrich,et al.  Neural Machine Translation of Rare Words with Subword Units , 2015, ACL.

[5]  Philip Gage,et al.  A new algorithm for data compression , 1994 .

[6]  Jason Weston,et al.  Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring , 2020, ICLR.

[7]  Claire Cardie,et al.  SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability , 2015, *SEMEVAL.

[8]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[9]  Samuel R. Bowman,et al.  Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks , 2018, ArXiv.

[10]  Eneko Agirre,et al.  *SEM 2013 shared task: Semantic Textual Similarity , 2013, *SEMEVAL.

[11]  Guillaume Lample,et al.  Cross-lingual Language Model Pretraining , 2019, NeurIPS.

[12]  Eneko Agirre,et al.  SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity , 2012, *SEMEVAL.

[13]  Iryna Gurevych,et al.  Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks , 2019, EMNLP.

[14]  Tomas Mikolov,et al.  Enriching Word Vectors with Subword Information , 2016, TACL.

[15]  Myle Ott,et al.  fairseq: A Fast, Extensible Toolkit for Sequence Modeling , 2019, NAACL.

[16]  Eneko Agirre,et al.  SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation , 2016, *SEMEVAL.

[17]  Eneko Agirre,et al.  SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation , 2017, *SEMEVAL.

[18]  Seungyoung Lim,et al.  KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension , 2019, ArXiv.

[19]  Guillaume Lample,et al.  XNLI: Evaluating Cross-lingual Sentence Representations , 2018, EMNLP.

[20]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[21]  Ray Kurzweil,et al.  Multilingual Universal Sentence Encoder for Semantic Retrieval , 2019, ACL.

[22]  Omer Levy,et al.  GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.

[23]  Claire Cardie,et al.  SemEval-2014 Task 10: Multilingual Semantic Textual Similarity , 2014, *SEMEVAL.

[24]  Yuta Hayashibe.  Japanese Realistic Textual Entailment Corpus , 2020, LREC.

[25]  Samuel R. Bowman,et al.  A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.

[26]  Christopher Potts,et al.  A large annotated corpus for learning natural language inference , 2015, EMNLP.

[27]  Holger Schwenk,et al.  Supervised Learning of Universal Sentence Representations from Natural Language Inference Data , 2017, EMNLP.