Scalable Cross-lingual Document Similarity through Language-specific Concept Hierarchies

• URL: http://arxiv.org/abs/2101.03026v1
• Date: Tue, 15 Dec 2020 10:42:40 GMT
• Title: Scalable Cross-lingual Document Similarity through Language-specific Concept Hierarchies
• Authors: Carlos Badenes-Olmedo, Jose-Luis Redondo Garc\'ia, Oscar Corcho
• Abstract summary: This paper presents an unsupervised document similarity algorithm that does not require parallel or comparable corpora. The algorithm annotates topics automatically created from documents in a single language with cross-lingual labels. Experiments performed on the English, Spanish and French editions of JCR-Acquis corpora reveal promising results on classifying and sorting documents by similar content.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the ongoing growth in number of digital articles in a wider set of languages and the expanding use of different languages, we need annotation methods that enable browsing multi-lingual corpora. Multilingual probabilistic topic models have recently emerged as a group of semi-supervised machine learning models that can be used to perform thematic explorations on collections of texts in multiple languages. However, these approaches require theme-aligned training data to create a language-independent space. This constraint limits the amount of scenarios that this technique can offer solutions to train and makes it difficult to scale up to situations where a huge collection of multi-lingual documents are required during the training phase. This paper presents an unsupervised document similarity algorithm that does not require parallel or comparable corpora, or any other type of translation resource. The algorithm annotates topics automatically created from documents in a single language with cross-lingual labels and describes documents by hierarchies of multi-lingual concepts from independently-trained models. Experiments performed on the English, Spanish and French editions of JCR-Acquis corpora reveal promising results on classifying and sorting documents by similar content.

Related papers

• Exploring syntactic information in sentence embeddings through multilingual subject-verb agreement [1.4335183427838039]
  We take the approach of developing curated synthetic data on a large scale, with specific properties. We use a new multiple-choice task and datasets, Blackbird Language Matrices, to focus on a specific grammatical structural phenomenon. We show that despite having been trained on multilingual texts in a consistent manner, multilingual pretrained language models have language-specific differences.
  arXiv  Detail & Related papers  (2024-09-10T14:58:55Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis [6.155943751502232]
  We present a language-independent graph neural network (GNN)-based model that achieves competitive results on common document layout datasets. Our model is suitable for industrial applications, particularly in multi-language scenarios.
  arXiv  Detail & Related papers  (2023-04-24T03:54:48Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models [107.83158521848372]
  We present textbfTriangular Document-level textbfPre-training (textbfTRIP), which is the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.
  arXiv  Detail & Related papers  (2022-12-15T12:14:25Z)
• Models and Datasets for Cross-Lingual Summarisation [78.56238251185214]
  We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German. We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles' bodies from language aligned Wikipedia titles.
  arXiv  Detail & Related papers  (2022-02-19T11:55:40Z)
• A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification [16.684856745734944]
  We present a multilingual bag-of-entities model that boosts the performance of zero-shot cross-lingual text classification. It leverages the multilingual nature of Wikidata: entities in multiple languages representing the same concept are defined with a unique identifier. A model trained on entity features in a resource-rich language can thus be directly applied to other languages.
  arXiv  Detail & Related papers  (2021-10-15T01:10:50Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.