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Found 15 papers, 9 papers with code
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures
Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation.
Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation
A recent trend in multilingual models is to not train on parallel data between all language pairs, but have a single bridge language, e. g. English.
A Large-Scale Test Set for the Evaluation of Context-Aware Pronoun Translation in Neural Machine Translation
We show that, while gains in BLEU are moderate for those systems, they outperform baselines by a large margin in terms of accuracy on our contrastive test set.
Domain Robustness in Neural Machine Translation
Domain robustness---the generalization of models to unseen test domains---is low for both statistical (SMT) and neural machine translation (NMT).
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%.
On Biasing Transformer Attention Towards Monotonicity
Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining.
A New Dataset and Efficient Baselines for Document-level Text Simplification in German
The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity.
Exploring German Multi-Level Text Simplification
We report on experiments in automatic text simplification (ATS) for German with multiple simplification levels along the Common European Framework of Reference for Languages (CEFR), simplifying standard German into levels A1, A2 and B1.
German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset
The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks.
The Word Sense Disambiguation Test Suite at WMT18
We evaluate all German{--}English submissions to the WMT{'}18 shared translation task, plus a number of submissions from previous years, and find that performance on the task has markedly improved compared to the 2016 WMT submissions (81{\%}→93{\%} accuracy on the WSD task).
A tree is a Baum is an \'arbol is a sach'a: Creating a trilingual treebank
This paper describes the process of constructing a trilingual parallel treebank.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition
Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion.
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.
Considerations for meaningful sign language machine translation based on glosses
Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021).
