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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval.

Kai Feng1, Lan Huang1, Hao Xu1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)
|July 27, 2022
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Summary

This study introduces MDL, a deep multilabel multilingual document learning method for cross-lingual document retrieval. MDL creates a shared semantic space, outperforming existing methods by using document-level comparisons and multilabel signals.

Keywords:
cross-lingual document representationcross-lingual document retrievalcross-lingual features

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Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Machine Learning

Background:

  • Cross-lingual document retrieval (CLDR) aims to find documents in one language using queries from another.
  • Existing CLDR methods often rely on word-level comparisons, neglecting crucial structural information.
  • This limitation leads to insufficient document representation and retrieval accuracy.

Purpose of the Study:

  • To develop a novel method for cross-lingual document retrieval that overcomes limitations of word-level comparisons.
  • To introduce a document-level comparison approach using a shared semantic space.
  • To enhance retrieval performance through enriched document representations.

Main Methods:

  • Proposed MDL (deep multilabel multilingual document learning), a six-layer fully connected network.
  • Projected cross-lingual documents into a shared semantic space for distance calculation.
  • Utilized automatically extracted multilabel supervision signals to construct the semantic space, avoiding manual label ambiguity.

Main Results:

  • MDL successfully projects cross-lingual documents into a unified semantic space.
  • Multilabel supervision signals enriched the semantic space representation, improving embedding discriminative ability.
  • Experiments on Wikipedia data demonstrated superior performance compared to state-of-the-art CLDR methods.

Conclusions:

  • MDL offers an effective approach to cross-lingual document retrieval by leveraging document-level semantics.
  • The method's reliance on multilabel signals enhances representation and retrieval accuracy.
  • MDL is efficient, scalable, and adaptable to various domains beyond its initial application.