Textual Representations for Crosslingual Information Retrieval
In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adopted the subword and character level representations for information retrieval that had shown to improve neural machine translation by reducing the out-of-vocabulary issues in machine translation. We found that crosslingual information retrieval performance can be improved by combining search results from subwords and token level representation.Additionally, we improved the search performance by combining and re-ranking the result sets from the different text representations for German, French and Japanese.
Code (0)
등록된 구현이 없습니다.
Tasks
Cross-Lingual Information RetrievalInformation RetrievalMachine TranslationRe-RankingRetrievalTranslationSimilar Papers 제목 키워드 기반
Low-Resource Parsing with Crosslingual Contextualized Representations
Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs), and compare them for crosslingual trans…
Dependency ParsingCrosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments
Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data. An approach to overcome this issue is to use unlab…
Event ExtractionRelationRepresentation LearningTransfer LearningFully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data
We present a fully unsupervised crosslingual semantic textual similarity (STS) metric, based on contextual embeddings extracted from BERT {--} Bidirectional Encoder Representations from Transformers (Devlin et al., 2019)…
Machine TranslationNatural Language UnderstandingSemantic Textual SimilaritySTS+1Distilling Monolingual and Crosslingual Word-in-Context Representations
In this study, we propose a method that distils representations of word meaning in context from a pre-trained masked language model in both monolingual and crosslingual settings. Word representations are the basis for co…
Language ModelingLanguage ModellingSemantic Textual SimilaritySTSCrosslingual Document Embedding as Reduced-Rank Ridge Regression
There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and …
Document EmbeddingregressionRetrievalSentence