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Adapting BigScience Multilingual Model to Unseen Languages ...
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On Efficiently Acquiring Annotations for Multilingual Models ...
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Team ÚFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models ...
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Does Corpus Quality Really Matter for Low-Resource Languages? ...
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IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for identification of offensive content in south Indian languages ...
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mSLAM: Massively multilingual joint pre-training for speech and text ...
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On the Representation Collapse of Sparse Mixture of Experts ...
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Politics and Virality in the Time of Twitter: A Large-Scale Cross-Party Sentiment Analysis in Greece, Spain and United Kingdom ...
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L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models ...
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Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts ...
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A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model ...
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A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
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Factual Consistency of Multilingual Pretrained Language Models ...
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Examining Scaling and Transfer of Language Model Architectures for Machine Translation ...
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Abstract:
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separate layer stacks for input and output processing. In machine translation, EncDec has long been the favoured approach, but with few studies investigating the performance of LMs. In this work, we thoroughly examine the role of several architectural design choices on the performance of LMs on bilingual, (massively) multilingual and zero-shot translation tasks, under systematic variations of data conditions and model sizes. Our results show that: (i) Different LMs have different scaling properties, where architectural differences often have a significant impact on model performance at small scales, but the performance gap narrows as the number of parameters increases, (ii) Several design choices, including causal masking and language-modeling objectives for the ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2202.00528 https://dx.doi.org/10.48550/arxiv.2202.00528
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MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset ...
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Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi ...
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From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction ...
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