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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 Hits 1 – 8 of 8 1 Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ... Zhu, Yi; Shareghi, Ehsan; Li, Yingzhen. - : arXiv, 2021 BASE Show details 2 It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Gholamreza; Arthur, Philip. - : Underline Science Inc., 2021 BASE Show details 3 A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Hinrich; Korhonen, Anna; Reichart, Roi; Shareghi, Ehsan; Vulić, Ivan; Zhao, Mengjie; Zhu, Yi. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.447 Abstract: Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25886-a-closer-look-at-few-shot-crosslingual-transfer-the-choice-of-shots-matters https://dx.doi.org/10.48448/m8s0-3a39 BASE Hide details 4 Self-Alignment Pretraining for Biomedical Entity Representations Liu, Fangyu; Shareghi, Ehsan; Meng, Zaiqiao. - : Association for Computational Linguistics, 2021. : Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021 BASE Show details 5 A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ... Zhao, Mengjie; Zhu, Yi; Shareghi, Ehsan. - : arXiv, 2020 BASE Show details 6 Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ... Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 7 Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees ... Shareghi, Ehsan; Petri, Matthias; Haffari, Gholamreza. - : arXiv, 2016 BASE Show details 8 Structured Prediction of Sequences and Trees using Infinite Contexts ... Shareghi, Ehsan; Haffari, Gholamreza; Cohn, Trevor. - : arXiv, 2015 BASE Show details

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