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XTREME-S: Evaluating Cross-lingual Speech Representations ...
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mSLAM: Massively multilingual joint pre-training for speech and text ...
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Larger-Scale Transformers for Multilingual Masked Language Modeling ...
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Multilingual Speech Translation from Efficient Finetuning of Pretrained Models ...
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Unsupervised Cross-lingual Representation Learning for Speech Recognition ...
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Multilingual Speech Translation with Efficient Finetuning of Pretrained Models ...
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Unsupervised Cross-lingual Representation Learning at Scale ...
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Emerging Cross-lingual Structure in Pretrained Language Models ...
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Specializing distributional vectors of all words for lexical entailment
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What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
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In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018)
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Abstract:
International audience ; Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. "Downstream" tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.archives-ouvertes.fr/hal-01898412
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XNLI: Evaluating Cross-lingual Sentence Representations ...
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
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Very Deep Convolutional Networks for Text Classification
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In: European Chapter of the Association for Computational Linguistics EACL'17 ; https://hal.archives-ouvertes.fr/hal-01454940 ; European Chapter of the Association for Computational Linguistics EACL'17, 2017, Valencia, Spain (2017)
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What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties
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