A General-Purpose Multilingual Document Encoder

• URL: http://arxiv.org/abs/2305.07016v1
• Date: Thu, 11 May 2023 17:55:45 GMT
• Title: A General-Purpose Multilingual Document Encoder
• Authors: Onur Galo\u{g}lu and Robert Litschko and Goran Glava\v{s}
• Abstract summary: We pretrain a massively multilingual document encoder as a hierarchical transformer model (HMDE) We leverage Wikipedia as a readily available source of comparable documents for creating training data. We evaluate the effectiveness of HMDE in two arguably most common and prominent cross-lingual document-level tasks.
• Score: 9.868221447090855
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Massively multilingual pretrained transformers (MMTs) have tremendously pushed the state of the art on multilingual NLP and cross-lingual transfer of NLP models in particular. While a large body of work leveraged MMTs to mine parallel data and induce bilingual document embeddings, much less effort has been devoted to training general-purpose (massively) multilingual document encoder that can be used for both supervised and unsupervised document-level tasks. In this work, we pretrain a massively multilingual document encoder as a hierarchical transformer model (HMDE) in which a shallow document transformer contextualizes sentence representations produced by a state-of-the-art pretrained multilingual sentence encoder. We leverage Wikipedia as a readily available source of comparable documents for creating training data, and train HMDE by means of a cross-lingual contrastive objective, further exploiting the category hierarchy of Wikipedia for creation of difficult negatives. We evaluate the effectiveness of HMDE in two arguably most common and prominent cross-lingual document-level tasks: (1) cross-lingual transfer for topical document classification and (2) cross-lingual document retrieval. HMDE is significantly more effective than (i) aggregations of segment-based representations and (ii) multilingual Longformer. Crucially, owing to its massively multilingual lower transformer, HMDE successfully generalizes to languages unseen in document-level pretraining. We publicly release our code and models at https://github.com/ogaloglu/pre-training-multilingual-document-encoders .

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