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Delving Deeper into Cross-lingual Visual Question Answering ...
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IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ...
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Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ...
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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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Abstract:
The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are ... : EMNLP 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2005.00052 https://dx.doi.org/10.48550/arxiv.2005.00052
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
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AdapterHub: A Framework for Adapting Transformers
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Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020
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Specialising Distributional Vectors of All Words for Lexical Entailment ...
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Specializing distributional vectors of all words for lexical entailment
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A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval
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