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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...7 Hits 1 – 20 of 124 1 MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset ... Nielsen, Dan Saattrup; McConville, Ryan. - : arXiv, 2022 BASE Show details 2 From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction ... Vacareanu, Robert; Valenzuela-Escarcega, Marco A.; Barbosa, George C. G.. - : arXiv, 2022 BASE Show details 3 Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations ... Meng, Yu; Zhang, Yunyi; Huang, Jiaxin. - : arXiv, 2022 BASE Show details 4 Shedding New Light on the Language of the Dark Web ... Jin, Youngjin; Jang, Eugene; Lee, Yongjae. - : arXiv, 2022 BASE Show details 5 LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback ... Zhu, Yunchang; Pang, Liang; Lan, Yanyan. - : arXiv, 2022 BASE Show details 6 Improving Word Translation via Two-Stage Contrastive Learning ... Li, Yaoyiran; Liu, Fangyu; Collier, Nigel. - : arXiv, 2022 BASE Show details 7 nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models ... Nigam, Shubham Kumar; Goel, Navansh. - : arXiv, 2022 BASE Show details 8 Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction ... Hofstätter, Sebastian; Khattab, Omar; Althammer, Sophia. - : arXiv, 2022 BASE Show details 9 LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval ... Xu, Canwen; Guo, Daya; Duan, Nan. - : arXiv, 2022 BASE Show details 10 Machine Learning for Food Review and Recommendation ... Le, Tan Khang; Hui, Siu Cheung. - : arXiv, 2022 BASE Show details 11 Zero-Shot Open Information Extraction using Question Generation and Reading Comprehension ... Gupta, Himanshu; Badugu, Amogh; Agrawal, Tamanna. - : arXiv, 2021 BASE Show details 12 Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies ... Pergola, Gabriele; Kochkina, Elena; Gui, Lin. - : arXiv, 2021 BASE Show details 13 How to Query Language Models? ... Adolphs, Leonard; Dhuliawala, Shehzaad; Hofmann, Thomas. - : arXiv, 2021 BASE Show details 14 Personalized Transformer for Explainable Recommendation ... Li, Lei; Zhang, Yongfeng; Chen, Li. - : arXiv, 2021 BASE Show details 15 Improving Authorship Verification using Linguistic Divergence ... Zhang, Yifan; Boumber, Dainis; Hosseinia, Marjan. - : arXiv, 2021 BASE Show details 16 On the Calibration and Uncertainty of Neural Learning to Rank Models ... Penha, Gustavo; Hauff, Claudia. - : arXiv, 2021 BASE Show details 17 A Comparison of Latent Semantic Analysis and Correspondence Analysis of Document-Term Matrices ... Qi, Qianqian; Hessen, David J.; Deoskar, Tejaswini. - : arXiv, 2021 BASE Show details 18 Leveraging Multilingual Transformers for Hate Speech Detection ... Roy, Sayar Ghosh; Narayan, Ujwal; Raha, Tathagata. - : arXiv, 2021 BASE Show details 19 NewsEmbed: Modeling News through Pre-trained Document Representations ... Liu, Jialu; Liu, Tianqi; Yu, Cong. - : arXiv, 2021 Abstract: Effectively modeling text-rich fresh content such as news articles at document-level is a challenging problem. To ensure a content-based model generalize well to a broad range of applications, it is critical to have a training dataset that is large beyond the scale of human labels while achieving desired quality. In this work, we address those two challenges by proposing a novel approach to mine semantically-relevant fresh documents, and their topic labels, with little human supervision. Meanwhile, we design a multitask model called NewsEmbed that alternatively trains a contrastive learning with a multi-label classification to derive a universal document encoder. We show that the proposed approach can provide billions of high quality organic training examples and can be naturally extended to multilingual setting where texts in different languages are encoded in the same semantic space. We experimentally demonstrate NewsEmbed's competitive performance across multiple natural language understanding tasks, both ... : Accepted in SIGKDD 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2106.00590 https://arxiv.org/abs/2106.00590 BASE Hide details 20 A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models ... Alam, Firoj; Hasan, Arid; Alam, Tanvirul. - : arXiv, 2021 BASE Show details Page: 1 2 3 4 5...7

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