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Found 9 papers, 2 papers with code
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas.
Machine Translation Between High-resource Languages in a Language Documentation Setting
This translation task is challenging for multiple reasons: (1) the data is out-of-domain with respect to the MT system’s training data, (2) much of the data is conversational, (3) existing translations include non-standard and uncommon expressions, often reflecting properties of the documented language, and (4) the data includes borrowings from other regional languages.
Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next
Human–computer conversation has long been an interest of artificial intelligence and natural language processing research.
Since the Scientific Literature Is Multilingual, Our Models Should Be Too
English has long been assumed the $\textit{lingua franca}$ of scientific research, and this notion is reflected in the natural language processing (NLP) research involving scientific document representation.
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models
However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data.
How to Adapt Your Pretrained Multilingual Model to 1600 Languages
Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining.
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Continued pretraining offers improvements, with an average accuracy of 44. 05%.
Athena: Constructing Dialogues Dynamically with Discourse Constraints
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.
