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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...613 Hits 1 – 20 of 12.249 1 Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice ... Borg, Markus; Bengtsson, Johan; Österling, Harald. - : arXiv, 2022 BASE Show details 2 Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang ... Keidar, Daphna; Opedal, Andreas; Jin, Zhijing. - : arXiv, 2022 BASE Show details 3 Similarity between person roles in a card sorting experiment ... Maldonado, Mora. - : Open Science Framework, 2022 BASE Show details 4 SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations ... Niu, Changan; Li, Chuanyi; Ng, Vincent; Ge, Jidong; Huang, Liguo; Luo, Bin. - : arXiv, 2022 Abstract: Recent years have seen the successful application of large pre-trained models to code representation learning, resulting in substantial improvements on many code-related downstream tasks. But there are issues surrounding their application to SE tasks. First, the majority of the pre-trained models focus on pre-training only the encoder of the Transformer. For generation tasks that are addressed using models with the encoder-decoder architecture, however, there is no reason why the decoder should be left out during pre-training. Second, many existing pre-trained models, including state-of-the-art models such as T5-learning, simply reuse the pre-training tasks designed for natural languages. Moreover, to learn the natural language description of source code needed eventually for code-related tasks such as code summarization, existing pre-training tasks require a bilingual corpus composed of source code and the associated natural language description, which severely limits the amount of data for pre-training. To ... : Accepted by ICSE 2022, not the final version ... Keyword: FOS Computer and information sciences; Software Engineering cs.SE URL: https://arxiv.org/abs/2201.01549 https://dx.doi.org/10.48550/arxiv.2201.01549 BASE Hide details 5 Cross-Lingual Phrase Retrieval ... Zheng, Heqi; Zhang, Xiao; Chi, Zewen. - : arXiv, 2022 BASE Show details 6 Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate ... Lenti, Jacopo; Ruffo, Giancarlo. - : arXiv, 2022 BASE Show details 7 Improve Sentence Alignment by Divide-and-conquer ... Zhang, Wu. - : arXiv, 2022 BASE Show details 8 Graph Neural Networks for Multiparallel Word Alignment ... Imani, Ayyoob; Şenel, Lütfi Kerem; Sabet, Masoud Jalili. - : arXiv, 2022 BASE Show details 9 Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ... Lai, Siyu; Yang, Zhen; Meng, Fandong. - : arXiv, 2022 BASE Show details 10 Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean ... Paschoal, André F. A.; Pirozelli, Paulo; Freire, Valdinei. - : arXiv, 2022 BASE Show details 11 A comparative study of several parameterizations for speaker recognition ... Faundez-Zanuy, Marcos. - : arXiv, 2022 BASE Show details 12 A Neural Pairwise Ranking Model for Readability Assessment ... Lee, Justin; Vajjala, Sowmya. - : arXiv, 2022 BASE Show details 13 A bilingual approach to specialised adjectives through word embeddings in the karstology domain ... Simeunović, Larisa Grčić; Martinc, Matej; Vintar, Špela. - : arXiv, 2022 BASE Show details 14 Speaker verification in mismatch training and testing conditions ... Faundez-Zanuy, Marcos; Slupinski, Adam. - : arXiv, 2022 BASE Show details 15 Universal Conditional Masked Language Pre-training for Neural Machine Translation ... Li, Pengfei; Li, Liangyou; Zhang, Meng. - : arXiv, 2022 BASE Show details 16 SMDT: Selective Memory-Augmented Neural Document Translation ... Zhang, Xu; Yang, Jian; Huang, Haoyang. - : arXiv, 2022 BASE Show details 17 Learning How to Translate North Korean through South Korean ... Kim, Hwichan; Moon, Sangwhan; Okazaki, Naoaki. - : arXiv, 2022 BASE Show details 18 When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? ... Mao, Zhuoyuan; Chu, Chenhui; Dabre, Raj. - : arXiv, 2022 BASE Show details 19 Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ... Zhang, Songming; Liu, Yijin; Meng, Fandong. - : arXiv, 2022 BASE Show details 20 Can Synthetic Translations Improve Bitext Quality? ... Briakou, Eleftheria; Carpuat, Marine. - : arXiv, 2022 BASE Show details Page: 1 2 3 4 5...613

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