1,673 research outputs found
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the first step towards development of effective CLIR models for language pairs and domains where parallel data are scarce or non-existent
Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been based
on linear transformations between the sets of embedding vectors in the two
languages. In this paper, we propose an approach that instead expresses the two
monolingual embedding spaces as probability densities defined by a Gaussian
mixture model, and matches the two densities using a method called normalizing
flow. The method requires no explicit supervision, and can be learned with only
a seed dictionary of words that have identical strings. We argue that this
formulation has several intuitively attractive properties, particularly with
the respect to improving robustness and generalization to mappings between
difficult language pairs or word pairs. On a benchmark data set of bilingual
lexicon induction and cross-lingual word similarity, our approach can achieve
competitive or superior performance compared to state-of-the-art published
results, with particularly strong results being found on etymologically distant
and/or morphologically rich languages.Comment: Accepted by NAACL-HLT 201
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
While billions of non-English speaking users rely on search engines every
day, the problem of ad-hoc information retrieval is rarely studied for
non-English languages. This is primarily due to a lack of data set that are
suitable to train ranking algorithms. In this paper, we tackle the lack of data
by leveraging pre-trained multilingual language models to transfer a retrieval
system trained on English collections to non-English queries and documents. Our
model is evaluated in a zero-shot setting, meaning that we use them to predict
relevance scores for query-document pairs in languages never seen during
training. Our results show that the proposed approach can significantly
outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and
Spanish. We also show that augmenting the English training collection with some
examples from the target language can sometimes improve performance.Comment: ECIR 2020 (short
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