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Found 16 papers, 3 papers with code
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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.
Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages.
Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers
In recent years machine translation has become very successful for high-resource language pairs.
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text
For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best.
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages
Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation.
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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%.
Tackling the Low-resource Challenge for Canonical Segmentation
Canonical morphological segmentation consists of dividing words into their standardized morphemes.
The IMS--CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS--CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020).
The IMS-CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS-CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020).
GPT-too: A language-model-first approach for AMR-to-text generation
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
Ranked #10 on
AMR-to-Text Generation
on LDC2017T10
Subword-Level Language Identification for Intra-Word Code-Switching
Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumption of a single language per token.
Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available.
Challenges of language technologies for the indigenous languages of the Americas
Indigenous languages of the American continent are highly diverse.
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce.
