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The objective of the Translation Inference Across Dictionaries (TIAD) shared task is to explore and compare methods and techniques 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 translations automatically among three languages - English, French, Portuguese - based on known indirect translations contained in the Apertium RDF graph. The evaluation of the results was carried out by the organisers against manually compiled language pairs of K Dictionaries. This paper gives an overall description of the shard task, the evaluation data and methodology, and the systems’ results.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA. Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.