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Found 10 papers, 6 papers with code
Signed Coreference Resolution
Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing.
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language.
Interpreting Language Models with Contrastive Explanations
Model interpretability methods are often used to explain NLP model decisions on tasks such as text classification, where the output space is relatively small.
When Does Translation Require Context? A Data-driven, Multilingual Exploration
Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics.
When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language.
Data Augmentation for Sign Language Gloss Translation
Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss-to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed.
Data Augmentation
Low-Resource Neural Machine Translation
+3
Do Context-Aware Translation Models Pay the Right Attention?
Are models paying large amounts of attention to the same context?
Including Signed Languages in Natural Language Processing
Signed languages are the primary means of communication for many deaf and hard of hearing individuals.
Measuring and Increasing Context Usage in Context-Aware Machine Translation
Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -- context from sentences other than those currently being translated.
Better Sign Language Translation with STMC-Transformer
This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language.
Ranked #1 on
Sign Language Translation
on ASLG-PC12
(using extra training data)
