Fetching the paper…
Reading the bibliography…
Complex word identification (CWI) is a cornerstone process towards proper text simplification.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Decision templates for multiple classifier fusion: an experimental comparison
Ludmila I Kuncheva, James C Bezdek, and Robert PW Duin. 2001 · 2001
Earlier work this paper cites.
Revisiting squared-error and cross-entropy functions for training neural network classifiers
Douglas M Kline and Victor L Berardi. 2005 · 2005
Earlier work this paper cites.
Ensemble based systems in decision making
Robi Polikar. 2006 · 2006
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2015
Earlier work this paper cites.
On loss functions for deep neural networks in classification
Katarzyna Janocha and Wojciech Marian Czarnecki. 2016 · 2016
Earlier work this paper cites.
Semeval 2016 task 11: Complex word identification
Gustavo Paetzold and Lucia Specia. 2016b · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Cross-lingual transfer learning for pos tagging without cross-lingual resources
Joo-Kyung Kim, Young-Bum Kim, Ruhi Sarikaya, and Eric Fosler-Lussier. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Cross-lingual complex word identification with multitask learning
Joachim Bingel and Johannes Bjerva. 2018 · 2018
Earlier work this paper cites.
Adversarial deep averaging networks for cross-lingual sentiment classification
Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, and Kilian Weinberger. 2018 · 2018
Cited alongside, same era.
Deep learning architecture for complex word identification
Dirk De Hertog and Anaïs Tack. 2018 · 2018
Cited alongside, same era.
Camb at cwi shared task 2018: Complex word identification with ensemble-based voting
Sian Gooding and Ekaterina Kochmar. 2018 · 2018
Cited alongside, same era.
Complex word identification based on frequency in a learner corpus
Tomoyuki Kajiwara and Mamoru Komachi. 2018 · 2018
Cited alongside, same era.
A word-complexity lexicon and a neural readability ranking model for lexical simplification
Mounica Maddela and Wei Xu. 2018 · 2018
Cited alongside, same era.
A report on the complex word identification shared task 2018
Sar target recognition based on cross-domain and cross-task transfer learning
Ke Wang, Gong Zhang, and Henry Leung. 2019 · 2019
Later among the works it cites.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Later among the works it cites.
Masking actor information leads to fairer political claims detection
Erenay Dayanik and Sebastian Padó. 2020 · 2020
Later among the works it cites.
Adversarial and domain-aware bert for cross-domain sentiment analysis
Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020 · 2020
Later among the works it cites.
Complex—a new corpus for lexical complexity predicition from likertscale data
Matthew Shardlow, Marcos Zampieri, and Michael Cooper. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Seid Muhie Yimam, Chris Biemann, Shervin Malmasi, Gustavo Paetzold, Lucia Specia, Sanja Štajner, Anaïs Tack, and Marcos Zampieri. 2018 · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R Sabuncu. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Strong baselines for complex word identification across multiple languages
Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, and Andreas Vlachos. 2019 · 2019
Cited alongside, same era.
Complex word identification as a sequence labelling task
Sian Gooding and Ekaterina Kochmar. 2019 · 2019
Cited alongside, same era.
Cross-lingual multi-level adversarial transfer to enhance low-resource name tagging
Lifu Huang, Heng Ji, and Jonathan May. 2019 · 2019
Cited alongside, same era.
Adversarial learning with contextual embeddings for zero-resource cross-lingual classification and ner
Phillip Keung, Vikas Bhardwaj, et al. 2019 · 2019
Cited alongside, same era.
Yuhua Tang, Zhipeng Lin, Haotian Wang, and Liyang Xu. 2020 · 2020
Later among the works it cites.
Domain adversarial fine-tuning as an effective regularizer
Giorgos Vernikos, Katerina Margatina, Alexandra Chronopoulou, and Ion Androutsopoulos. 2020 · 2020
Later among the works it cites.
Cross-lingual transfer learning for complex word identification
George-Eduard Zaharia, Dumitru-Clementin Cercel, and Mihai Dascalu. 2020 · 2020
Later among the works it cites.
Unsupervised domain adaptation for cross-lingual text labeling
Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero dos Santos, Kathleen McKeown, and Bing Xiang. 2020 · 2020
Later among the works it cites.
C3sl at semeval-2021 task 1: Predicting lexical complexity of words in specific contexts with sentence embeddings
Raul Almeida, Hegler Tissot, and Marcos Didonet Del Fabro. 2021 · 2021
Later among the works it cites.
A brief review of domain adaptation
Abolfazl Farahani, Sahar Voghoei, Khaled Rasheed, and Hamid R Arabnia. 2021 · 2021
Later among the works it cites.
Improved multi-source domain adaptation by preservation of factors
Sebastian Schrom, Stephan Hasler, and Jürgen Adamy. 2021 · 2021
Later among the works it cites.
Semeval-2021 task 1: Lexical complexity prediction
Matthew Shardlow, Richard Evans, Gustavo Paetzold, and Marcos Zampieri. 2021a · 2021
Later among the works it cites.
Upb at semeval-2021 task 1: Combining deep learning and hand-crafted features for lexical complexity prediction
George-Eduard Zaharia, Dumitru-Clementin Cercel, and Mihai Dascalu. 2021 · 2021
Later among the works it cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
Closest in time.