The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer
暂无分享,去创建一个
[1] C. Clarke,et al. Early Stage Sparse Retrieval with Entity Linking , 2022, CIKM.
[2] Antonios Anastasopoulos,et al. Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering , 2021, MRQA.
[3] Carsten Eickhoff,et al. IsoScore: Measuring the Uniformity of Embedding Space Utilization , 2021, FINDINGS.
[4] Matt Gardner,et al. QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension , 2021, ACM Comput. Surv..
[5] Mitesh M. Khapra,et al. A Primer on Pretrained Multilingual Language Models , 2021, ArXiv.
[6] Ming-Wei Chang,et al. Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer , 2021, ArXiv.
[7] Suzan Verberne,et al. Can BERT Dig It? Named Entity Recognition for Information Retrieval in the Archaeology Domain , 2021, ACM Journal on Computing and Cultural Heritage.
[8] Ngoc Thang Vu,et al. AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages , 2021, ACL.
[9] Jinlan Fu,et al. XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation , 2021, EMNLP.
[10] Simone Paolo Ponzetto,et al. Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval , 2021, ECIR.
[11] P. Kostenetskiy,et al. HPC Resources of the Higher School of Economics , 2021, Journal of Physics: Conference Series.
[12] Faizan Javed,et al. An End-to-End Solution for Named Entity Recognition in eCommerce Search , 2020, AAAI.
[13] Noha Abdelrahman. Text Mining for Precision Medicine : Natural Language Processing, Machine Learning and Information Extraction for Knowledge Discovery in the Health Domain , 2020 .
[14] Goran Glavaš,et al. From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers , 2020, EMNLP.
[15] Hinrich Schütze,et al. Identifying Elements Essential for BERT’s Multilinguality , 2020, EMNLP.
[16] Benoit Sagot,et al. When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models , 2020, NAACL.
[17] Jacopo Staiano,et al. Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering , 2020, EMNLP.
[18] Isabelle Augenstein,et al. Unsupervised Evaluation for Question Answering with Transformers , 2020, BLACKBOXNLP.
[19] José Luis Redondo García,et al. Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study , 2020, FINDINGS.
[20] Steffen Eger,et al. Inducing Language-Agnostic Multilingual Representations , 2020, STARSEM.
[21] Christopher D. Manning,et al. Finding Universal Grammatical Relations in Multilingual BERT , 2020, ACL.
[22] Dan Roth,et al. Extending Multilingual BERT to Low-Resource Languages , 2020, FINDINGS.
[23] Orhan Firat,et al. XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization , 2020, ICML.
[24] Eunsol Choi,et al. TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages , 2020, Transactions of the Association for Computational Linguistics.
[25] Dan Klein,et al. Multilingual Alignment of Contextual Word Representations , 2020, ICLR.
[26] Dan Roth,et al. Cross-Lingual Ability of Multilingual BERT: An Empirical Study , 2019, ICLR.
[27] Tian Wang,et al. Building Large-Scale Deep Learning System for Entity Recognition in E-Commerce Search , 2019, BDCAT.
[28] Meng-Han Tsai,et al. Question-answering dialogue system for emergency operations , 2019 .
[29] Alexander M. Fraser,et al. How Language-Neutral is Multilingual BERT? , 2019, ArXiv.
[30] Luke Zettlemoyer,et al. Emerging Cross-lingual Structure in Pretrained Language Models , 2019, ACL.
[31] Mikel Artetxe,et al. On the Cross-lingual Transferability of Monolingual Representations , 2019, ACL.
[32] Holger Schwenk,et al. MLQA: Evaluating Cross-lingual Extractive Question Answering , 2019, ACL.
[33] J. Carbonell,et al. Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework , 2019, ICLR.
[34] Yijia Liu,et al. Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing , 2019, EMNLP.
[35] Betty van Aken,et al. How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations , 2019, CIKM.
[36] Hung-yi Lee,et al. Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model , 2019, EMNLP.
[37] Holger Schwenk,et al. WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia , 2019, EACL.
[38] Eva Schlinger,et al. How Multilingual is Multilingual BERT? , 2019, ACL.
[39] Graham Neubig,et al. Choosing Transfer Languages for Cross-Lingual Learning , 2019, ACL.
[40] Mark Dredze,et al. Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT , 2019, EMNLP.
[41] Mona T. Diab,et al. Context-Aware Cross-Lingual Mapping , 2019, NAACL.
[42] Regina Barzilay,et al. Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing , 2019, NAACL.
[43] Trevor Cohn,et al. Massively Multilingual Transfer for NER , 2019, ACL.
[44] Holger Schwenk,et al. Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond , 2018, Transactions of the Association for Computational Linguistics.
[45] Guillaume Lample,et al. XNLI: Evaluating Cross-lingual Sentence Representations , 2018, EMNLP.
[46] Anders Søgaard,et al. On the Limitations of Unsupervised Bilingual Dictionary Induction , 2018, ACL.
[47] Stefan Wermter,et al. Continual Lifelong Learning with Neural Networks: A Review , 2018, Neural Networks.
[48] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[49] Guillaume Lample,et al. Word Translation Without Parallel Data , 2017, ICLR.
[50] Anders Søgaard,et al. A Survey of Cross-lingual Word Embedding Models , 2017, J. Artif. Intell. Res..
[51] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[52] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[53] Ebrahim Bagheri,et al. Document Retrieval Model Through Semantic Linking , 2017, WSDM.
[54] Quoc V. Le,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[55] Jian Zhang,et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.
[56] Stephanie Strassel,et al. LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages , 2016, LREC.
[57] Quoc V. Le,et al. Exploiting Similarities among Languages for Machine Translation , 2013, ArXiv.
[58] Noah A. Smith,et al. A Simple, Fast, and Effective Reparameterization of IBM Model 2 , 2013, NAACL.
[59] Andrew Y. Ng,et al. Improving Word Representations via Global Context and Multiple Word Prototypes , 2012, ACL.
[60] Weiguo Fan,et al. Beyond keywords: Automated question answering on the web , 2008, CACM.
[61] Anthony V. Robins,et al. Catastrophic Forgetting, Rehearsal and Pseudorehearsal , 1995, Connect. Sci..
[62] R Ratcliff,et al. Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. , 1990, Psychological review.
[63] Genta Indra Winata,et al. Preserving Cross-Linguality of Pre-trained Models via Continual Learning , 2021, REPL4NLP.
[64] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[65] Heng Ji,et al. Cross-lingual Name Tagging and Linking for 282 Languages , 2017, ACL.
[66] Emanuele Della Valle,et al. An Introduction to Information Retrieval , 2013 .
[67] M. Kenward,et al. An Introduction to the Bootstrap , 2007 .
[68] Philipp Koehn,et al. Europarl: A Parallel Corpus for Statistical Machine Translation , 2005, MTSUMMIT.
[69] Rich Caruana,et al. Algorithms and Applications for Multitask Learning , 1996, ICML.
[70] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[71] David H. Bailey,et al. Algorithms and applications , 1988 .
[72] E. Pitman. Significance Tests Which May be Applied to Samples from Any Populations , 1937 .