Data for paper: "Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval"

Item Type:	Dataset
Title:	Data for paper: "Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval"
Date:	20 January 2021
Creator:	Litschko, Robert

DDC Classification:

004 Computer science, internet

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 based on CLWEs. For sentence-level CLIR, we demonstrate that state-of-the-art performance can be achieved. However, the peak performance is not met using the general-purpose multilingual text encoders `off-the-shelf', but rather relying on their variants that have been further specialized for sentence understanding tasks.

URL:	https://madata.bib.uni-mannheim.de/361/
DOI:	https://doi.org/10.7801/361
Availability (Controlled):	Unknown

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File	Filename / Infos	Link
	Archive Filename: mbert_iso_layer_0.tar.gz	Download (5GB)
	Archive Filename: mbert_aoc_layer_9.tar.gz	Download (5GB)
	Archive Filename: xlm_iso_layer_1.tar.gz	Download (9GB)
	Archive Filename: xlm_aoc_layer_12.tar.gz	Download (9GB)
	Archive Filename: xlm_aoc_layer_15.tar.gz	Download (9GB)
	Archive Filename: checkpoints.tar.gz	Download (7GB)

Depositing User:	Robert Litschko
Date Deposited:	22 Jan 2021 10:26
Last Modified:	29 Feb 2024 20:37

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