ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

• URL: http://arxiv.org/abs/2107.06632v2
• Date: Thu, 15 Jul 2021 08:23:08 GMT
• Title: ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus
• Authors: Ayyoob Imani, Masoud Jalili Sabet, Philipp Dufter, Michael Cysouw, Hinrich Sch\"utze
• Abstract summary: Researching typological properties of languages is fundamental for progress in multilingual NLP. We provide ParCourE, an online tool that allows to browse a word-aligned parallel corpus, covering 1334 languages.
• Score: 2.7036498789349244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective. Researching typological properties of languages is fundamental for progress in multilingual NLP. Examples include assessing language similarity for effective transfer learning, injecting inductive biases into machine learning models or creating resources such as dictionaries and inflection tables. We provide ParCourE, an online tool that allows to browse a word-aligned parallel corpus, covering 1334 languages. We give evidence that this is useful for typological research. ParCourE can be set up for any parallel corpus and can thus be used for typological research on other corpora as well as for exploring their quality and properties.

Related papers

• A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space [61.18554842370824]
  In cross-lingual language models, representations for many different languages live in the same space. We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance. We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
  arXiv  Detail & Related papers  (2021-09-13T21:05:37Z)
• Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes [0.2538209532048867]
  A novel Orthogonal Structural Probe (Limisiewicz and Marevcek, 2021) allows us to answer this question for specific linguistic features. We evaluate syntactic (UD) and lexical (WordNet) structural information encoded inmBERT's contextual representations for nine diverse languages. We successfully apply our findings to zero-shot and few-shot cross-lingual parsing.
  arXiv  Detail & Related papers  (2021-09-10T15:03:11Z)
• Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer [3.299672391663527]
  We analyze a state-of-the-art multilingual model and try to determine what impacts good transfer between languages. We show that looking at particular syntactic features is 2-4 times more helpful in predicting the performance than an aggregated syntactic similarity.
  arXiv  Detail & Related papers  (2021-05-12T21:22:58Z)
• Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling? [6.294759639481189]
  This paper describes our experiments and observations about the role of pre-trained and fine-tuned multilingual embeddings in performing multi-dimensional, multilingual language proficiency classification. Our results indicate that while fine-tuned embeddings are useful for multilingual proficiency modeling, none of the features achieve consistently best performance for all dimensions of language proficiency.
  arXiv  Detail & Related papers  (2021-02-25T16:23:52Z)
• Revisiting Language Encoding in Learning Multilingual Representations [70.01772581545103]
  We propose a new approach called Cross-lingual Language Projection (XLP) to replace language embedding. XLP projects the word embeddings into language-specific semantic space, and then the projected embeddings will be fed into the Transformer model. Experiments show that XLP can freely and significantly boost the model performance on extensive multilingual benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-16T18:47:10Z)
• Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures [73.06435180872293]
  We construct a recurrent neural network predictor based on byte embeddings and convolutional layers. We show that some features from various linguistic types can be predicted reliably.
  arXiv  Detail & Related papers  (2020-04-30T21:00:53Z)
• 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)
• Evaluating Transformer-Based Multilingual Text Classification [55.53547556060537]
  We argue that NLP tools perform unequally across languages with different syntactic and morphological structures. We calculate word order and morphological similarity indices to aid our empirical study.
  arXiv  Detail & Related papers  (2020-04-29T03:34:53Z)
• 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.