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Found 9 papers, 4 papers with code
Beyond Characters: Subword-level Morpheme Segmentation
This paper presents DeepSPIN’s submissions to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation.
Ranked #1 on
Morpheme Segmentaiton
on UniMorph 4.0
Did Translation Models Get More Robust Without Anyone Even Noticing?
Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to "noisy" inputs, such as spelling errors, abbreviations, and other formatting issues.
Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task.
Smoothing and Shrinking the Sparse Seq2Seq Search Space
Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.
One-Size-Fits-All Multilingual Models
For both tasks, we present multilingual models, training jointly on data in all languages.
IT--IST at the SIGMORPHON 2019 Shared Task: Sparse Two-headed Models for Inflection
This paper presents the Instituto de Telecomunica{\c{c}}{\~o}es{--}Instituto Superior T{\'e}cnico submission to Task 1 of the SIGMORPHON 2019 Shared Task.
Interpretable Structure Induction via Sparse Attention
Neural network methods are experiencing wide adoption in NLP, thanks to their empirical performance on many tasks.
Massively Multilingual Neural Grapheme-to-Phoneme Conversion
Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
