• Corpus ID: 208016292

Results of the Translation Inference Across Dictionaries 2019 Shared Task

@inproceedings{Gracia2019ResultsOT,
  title={Results of the Translation Inference Across Dictionaries 2019 Shared Task},
  author={Jorge Gracia and Besim Kabashi and Ilan Kernerman and Marta Lanau-Coronas and Dorielle Lonke},
  booktitle={TIAD@LDK},
  year={2019}
}
The objective of the Translation Inference Across Dictionar- ies (TIAD) shared task is to explore and compare methods and tech- niques that infer translations indirectly between language pairs, based on other bilingual/multilingual lexicographic resources. In its second, 2019, edition the participating systems were asked to generate new transla- tions automatically among three languages - English, French, Portuguese - based on known indirect translations contained in the Apertium RDF graph. The… 
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Multi-Strategy system for translation inference across dictionaries
TLDR
Four different strategies proposed to the TIAD 2020 Shared Task for automatic translation inference across dictionaries are described, based on the analysis of Apertium RDF graph, taking advantage of characteristics such as translation using multiple paths, synonyms and similarities between lexical entries from different lexicons and cardinality of possible translations through the graph.
Graph Exploration and Cross-lingual Word Embeddings for Translation Inference Across Dictionaries
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The task evaluation results show that graph exploration is very effective, accomplishing relatively high precision and recall values in comparison with the other participating systems, while cross-lingual word embeddings reaches high precision but smaller recall.
Bilingual dictionary generation and enrichment via graph exploration
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This work explores techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations) and builds upon a cycle density based method: partitioning the graph into biconnected components for a speedup, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements.
Linking Discourse Marker Inventories
TLDR
The paper describes the first comprehensive edition of machine-readable discourse marker lexicons, to explore techniques for translation inference to be applied to this particular group of lexical resources that was previously largely neglected in the context of Linguistic Linked (Open) Data.
NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference
TLDR
The NUIG system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an un supervised multi-way neural machine translation method at the TIAD shard task.
Basic Linguistic Resources and Baselines for Bhojpuri, Magahi and Maithili for Natural Language Processing
TLDR
This work collected corpora for these three languages from various sources and cleaned them to the extent possible, without changing the data in them, and calculated some basic statistical measures for these corpora at character, word, syllable, and morpheme levels to give an indication of linguistic properties such as morphological, lexical, phonological, and syntactic complexities.
Linguistic Resources for Bhojpuri, Magahi, and Maithili: Statistics about Them, Their Similarity Estimates, and Baselines for Three Applications
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The main contribution of the work is the creation of basic resources for facilitating further language processing research for these languages, providing some quantitative measures about them and their similarities among themselves and with Hindi.

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