Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation
暂无分享,去创建一个
[1] Phil Blunsom,et al. Reasoning about Entailment with Neural Attention , 2015, ICLR.
[2] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[3] Zhiguo Wang,et al. Bilateral Multi-Perspective Matching for Natural Language Sentences , 2017, IJCAI.
[4] Hermann Ney,et al. CharacTer: Translation Edit Rate on Character Level , 2016, WMT.
[5] Josef van Genabith,et al. ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural Networks , 2015, EMNLP.
[6] Zhen-Hua Ling,et al. Enhanced LSTM for Natural Language Inference , 2016, ACL.
[7] Maja Popovic,et al. chrF: character n-gram F-score for automatic MT evaluation , 2015, WMT@EMNLP.
[8] Paula Estrella,et al. Semantic Textual Similarity for MT evaluation , 2012, WMT@NAACL-HLT.
[9] Preslav Nakov,et al. Using Discourse Structure Improves Machine Translation Evaluation , 2014, ACL.
[10] Alon Lavie,et al. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.
[11] Timothy Baldwin,et al. Testing for Significance of Increased Correlation with Human Judgment , 2014, EMNLP.
[12] Khalil Sima'an,et al. BEER: BEtter Evaluation as Ranking , 2014, WMT@ACL.
[13] Philipp Koehn,et al. Findings of the 2017 Conference on Machine Translation (WMT17) , 2017, WMT.
[14] Qun Liu,et al. Blend: a Novel Combined MT Metric Based on Direct Assessment — CASICT-DCU submission to WMT17 Metrics Task , 2017, WMT.
[15] Ralph Weischedel,et al. A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION , 2005 .
[16] Hwee Tou Ng,et al. TESLA: Translation Evaluation of Sentences with Linear-Programming-Based Analysis , 2010, WMT@ACL.
[17] Karin M. Verspoor,et al. Findings of the 2016 Conference on Machine Translation , 2016, WMT.
[18] Daniel Jurafsky,et al. Robust Machine Translation Evaluation with Entailment Features , 2009, ACL.
[19] Timothy Baldwin,et al. Can machine translation systems be evaluated by the crowd alone , 2015, Natural Language Engineering.
[20] Rui Yan,et al. Natural Language Inference by Tree-Based Convolution and Heuristic Matching , 2015, ACL.
[21] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[22] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[23] Timothy Baldwin,et al. Accurate Evaluation of Segment-level Machine Translation Metrics , 2015, NAACL.
[24] Lluís Màrquez i Villodre,et al. Linguistic Features for Automatic Evaluation of Heterogenous MT Systems , 2007, WMT@ACL.
[25] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[26] Trevor Cohn,et al. Regression and ranking based optimisation for sentence level machine translation evaluation , 2011 .
[27] Chi-kiu Lo,et al. MEANT 2.0: Accurate semantic MT evaluation for any output language , 2017, WMT.
[28] Mamoru Komachi,et al. RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation , 2018, WMT.
[29] Percy Liang,et al. Know What You Don’t Know: Unanswerable Questions for SQuAD , 2018, ACL.
[30] Ondrej Bojar,et al. Results of the WMT17 Metrics Shared Task , 2017, WMT.