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Found 8 papers, 2 papers with code
Simple and effective data augmentation for compositional generalization
Compositional generalization, the ability to predict complex meanings from training on simpler sentences, poses challenges for powerful pretrained seq2seq models.
Predicting generalization performance with correctness discriminators
We present a novel model that establishes upper and lower bounds on the accuracy, without requiring gold labels for the unseen data.
SLOG: A Structural Generalization Benchmark for Semantic Parsing
The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions.
Structural generalization is hard for sequence-to-sequence models
Sequence-to-sequence (seq2seq) models have been successful across many NLP tasks, including ones that require predicting linguistic structure.
Compositional Generalization Requires Compositional Parsers
A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into unseen sequences.
ELITR Non-Native Speech Translation at IWSLT 2020
This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020.
Dynamic Masking for Improved Stability in Spoken Language Translation
For spoken language translation (SLT) in live scenarios such as conferences, lectures and meetings, it is desirable to show the translation to the user as quickly as possible, avoiding an annoying lag between speaker and translated captions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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