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There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space.
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Multilingual universal sentence encoder for semantic retrieval
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Not Enough Data? Deep Learning to the Rescue!
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Rank Correlation Methods
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An empirical comparison of coefficient alpha, guttman’s lambda - 2, and msplit maximized split-half reliability estimates
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Efficient BackProp
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Combining labeled and unlabeled data with co-training
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
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Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
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Why Does Unsupervised Pre-training Help Deep Learning?
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Graph regularized transductive classification on heterogeneous information networks
Ming Ji, Yizhou Sun, Marina Danilevsky, Jiawei Han, and Jing Gao. 2010 · 2010
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Preference Uncertainty, Preference Learning, and Paired Comparison Experiments
David C. Kingsley and Thomas C. Brown. 2010 · 2010
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SemEval-2014 task 10: Multilingual semantic textual similarity
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014 · 2014
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Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, et al. 2015 · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2015 · 2015
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Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation
Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez Agirre, Rada Mihalcea, German Rigau Claramunt, and Janyce Wiebe. 2016 · 2016
Cross-topic argument mining from heterogeneous sources
Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, and Iryna Gurevych. 2018 · 2018
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Injecting relational structural representation in neural networks for question similarity
Antonio Uva, Daniele Bonadiman, and Alessandro Moschitti. 2018 · 2018
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QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018 · 2018
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Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha Talukdar. 2019 · 2019
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Aiming beyond the obvious: Identifying non-obvious cases in semantic similarity datasets
Nicole Peinelt, Maria Liakata, and Dong Nguyen. 2019 · 2019
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Capturing reliable fine-grained sentiment associations by crowdsourcing and best–worst scaling
Svetlana Kiritchenko and Saif M. Mohammad. 2016 · 2016
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Measuring the similarity of sentential arguments in dialogue
Amita Misra, Brian Ecker, and Marilyn A. Walker. 2016 · 2016
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
Nils Reimers and Iryna Gurevych. 2017 · 2017
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Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian. 2017 · 2017
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Universal sentence encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil. 2018 · 2018
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Classification and Clustering of Arguments with Contextualized Word Embeddings
Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. 2019 · 2019
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EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Jason Wei and Kai Zou. 2019 · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 2019
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Conditional bert contextual augmentation
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu. 2019 · 2019
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DiPair: Fast and accurate distillation for trillion-scale text matching and pair modeling
Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, and Ehsan Emadzadeh. 2020 · 2020
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Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring
Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le. 2020 · 2020
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