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1
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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2
Improving Word Translation via Two-Stage Contrastive Learning ...
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3
Towards Zero-shot Language Modeling ...
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4
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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5
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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6
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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7
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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8
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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9
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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10
Emergent Communication Pretraining for Few-Shot Machine Translation ...
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11
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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12
Verb Knowledge Injection for Multilingual Event Processing ...
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13
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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14
Probing Pretrained Language Models for Lexical Semantics ...
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15
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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16
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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17
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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18
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
Abstract: Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1807.00914
https://dx.doi.org/10.48550/arxiv.1807.00914
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19
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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20
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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