AgriBERT: Knowledge-Infused Agricultural Language Models for Matching Food and Nutrition
暂无分享,去创建一个
Tianming Liu | Zihao Wu | Bao Ge | Saed Rezayi | Sheng Li | Zheng-Long Liu | Chandra Dhakal | Chen Zhen
[1] Jianfeng Gao,et al. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing , 2020, ACM Trans. Comput. Heal..
[2] Eduard Hovy,et al. A Survey of Data Augmentation Approaches for NLP , 2021, FINDINGS.
[3] Handong Zhao,et al. Edge: Enriching Knowledge Graph Embeddings with External Text , 2021, NAACL.
[4] Soroush Vosoughi,et al. Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning , 2021, NAACL.
[5] Kevin Gimpel,et al. Substructure Substitution: Structured Data Augmentation for NLP , 2021, FINDINGS.
[6] Teruko Mitamura,et al. GenAug: Data Augmentation for Finetuning Text Generators , 2020, DEELIO.
[7] Yu Wang,et al. How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? , 2020, FINDINGS.
[8] Xiangji Huang,et al. Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task , 2020, LREC.
[9] Zhe Zhao,et al. K-BERT: Enabling Language Representation with Knowledge Graph , 2019, AAAI.
[10] Joey Tianyi Zhou,et al. Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment , 2019, AAAI.
[11] Jaewoo Kang,et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining , 2019, Bioinform..
[12] Marcin Junczys-Dowmunt,et al. Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data , 2019, BEA@ACL.
[13] Iryna Gurevych,et al. COALA: A Neural Coverage-Based Approach for Long Answer Selection with Small Data , 2019, AAAI.
[14] Graham Neubig,et al. Generalized Data Augmentation for Low-Resource Translation , 2019, ACL.
[15] Jimmy J. Lin,et al. End-to-End Open-Domain Question Answering with BERTserini , 2019, NAACL.
[16] Kai Zou,et al. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks , 2019, EMNLP.
[17] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[18] Damion M. Dooley,et al. FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration , 2018, npj Science of Food.
[19] Jieyu Zhao,et al. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods , 2018, NAACL.
[20] Richard Socher,et al. Learned in Translation: Contextualized Word Vectors , 2017, NIPS.
[21] Marcus D. Bloice,et al. Data Augmentation , 2017, Encyclopedia of Machine Learning and Data Mining.
[22] M. de Rijke,et al. Short Text Similarity with Word Embeddings , 2015, CIKM.
[23] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[24] Ming-Wei Chang,et al. Question Answering Using Enhanced Lexical Semantic Models , 2013, ACL.
[25] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[26] Ann Bies,et al. The Penn Treebank: Annotating Predicate Argument Structure , 1994, HLT.
[27] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.