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1
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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2
Geographic Adaptation of Pretrained Language Models ...
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3
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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4
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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5
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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6
AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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7
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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8
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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9
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer ...
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10
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ...
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11
Verb Knowledge Injection for Multilingual Event Processing ...
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12
Probing Pretrained Language Models for Lexical Semantics ...
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13
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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14
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
Abstract: Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero ... : EMNLP 2019 (Long paper) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1909.01638
https://arxiv.org/abs/1909.01638
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15
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
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16
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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17
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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18
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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19
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ...
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