Cross-Lingual Learning-to-Rank with Shared Representations
Shota Sasaki,Shuo Sun,Shigehiko Schamoni,Kevin Duh,Kentaro Inui +4 more
- Vol. 2, pp 458-463
TLDR
A large-scale dataset derived from Wikipedia is introduced to support CLIR research in 25 languages and a simple yet effective neural learning-to-rank model is presented that shares representations across languages and reduces the data requirement.Citations
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Cross-Language Information Retrieval.
Douglas W. Oard,Anne R. Diekema +1 more
TL;DR: This chapter reviews research and practice in CLIR that allows users to state queries in their native language and retrieve documents in any other language supported by the system.
Proceedings ArticleDOI
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
TL;DR: The authors compare encoders and decoders based on Recurrent Neural Networks (RNNs) and modified self-attentive architectures for cross-lingual transfer and show that RNN-based architectures transfer well to languages that are close to English and perform especially well on distant languages.
Proceedings ArticleDOI
XOR QA: Cross-lingual Open-Retrieval Question Answering
TL;DR: This work constructs a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for and introduces a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingually document retrieval from multilingual and English resources.
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On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
TL;DR: Investigating crosslingual transfer and posit that an orderagnostic model will perform better when transferring to distant foreign languages shows that RNN-based architectures transfer well to languages that are close to English, while self-attentive models have better overall cross-lingualtransferability and perform especially well on distant languages.
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FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
TL;DR: FILTER is proposed, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning and proposes an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
References
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Adam: A Method for Stochastic Optimization
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Distributed Representations of Words and Phrases and their Compositionality
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Proceedings ArticleDOI
A Deep Relevance Matching Model for Ad-hoc Retrieval
TL;DR: A novel deep relevance matching model (DRMM) for ad-hoc retrieval that employs a joint deep architecture at the query term level for relevance matching and can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.