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1
Delving Deeper into Cross-lingual Visual Question Answering ...
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2
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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3
Improving Word Translation via Two-Stage Contrastive Learning ...
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4
Towards Zero-shot Language Modeling ...
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5
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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6
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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7
Modelling Latent Translations for Cross-Lingual Transfer ...
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8
Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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9
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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10
xGQA: Cross-Lingual Visual Question Answering ...
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11
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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12
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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13
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
Abstract: Pretrained multilingual text encoders based on neural Transformer architectures, such as multilingual BERT (mBERT) and XLM, have achieved strong performance on a myriad of language understanding tasks. Consequently, they have been adopted as a go-to paradigm for multilingual and cross-lingual representation learning and transfer, rendering cross-lingual word embeddings (CLWEs) effectively obsolete. However, questions remain to which extent this finding generalizes 1) to unsupervised settings and 2) for ad-hoc cross-lingual IR (CLIR) tasks. Therefore, in this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a large number of language pairs. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR -- a setup with no relevance judgments for IR-specific fine-tuning -- pretrained encoders fail to significantly outperform models ... : accepted at ECIR'21 (preprint) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; H.3.3; I.2.7; Information Retrieval cs.IR
URL: https://dx.doi.org/10.48550/arxiv.2101.08370
https://arxiv.org/abs/2101.08370
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14
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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15
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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16
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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17
Emergent Communication Pretraining for Few-Shot Machine Translation ...
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18
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer ...
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19
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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20
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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