Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Mager, Manuel"'
Autor:
Shahriar, Sadat, Qi, Zheng, Pappas, Nikolaos, Doss, Srikanth, Sunkara, Monica, Halder, Kishaloy, Mager, Manuel, Benajiba, Yassine
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require ful
Externí odkaz:
http://arxiv.org/abs/2410.19206
Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an impor
Externí odkaz:
http://arxiv.org/abs/2306.06804
In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-resource languages, including Indigenous languages. However, the latte
Externí odkaz:
http://arxiv.org/abs/2305.19474
Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages. For the task of machine translation (MT), morphological segmentation has proven successful in allevi
Externí odkaz:
http://arxiv.org/abs/2210.06990
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. We investigate a wide variety of supervised and unsupervised morpholog
Externí odkaz:
http://arxiv.org/abs/2203.08954
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augmentation, multi-task and transfer learning approaches for the automatic s
Externí odkaz:
http://arxiv.org/abs/2106.16055
Autor:
Ebrahimi, Abteen, Mager, Manuel, Oncevay, Arturo, Chaudhary, Vishrav, Chiruzzo, Luis, Fan, Angela, Ortega, John, Ramos, Ricardo, Rios, Annette, Meza-Ruiz, Ivan, Giménez-Lugo, Gustavo A., Mager, Elisabeth, Neubig, Graham, Palmer, Alexis, Coto-Solano, Rolando, Vu, Ngoc Thang, Kann, Katharina
Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, synt
Externí odkaz:
http://arxiv.org/abs/2104.08726
Canonical morphological segmentation consists of dividing words into their standardized morphemes. Here, we are interested in approaches for the task when training data is limited. We compare model performance in a simulated low-resource setting for
Externí odkaz:
http://arxiv.org/abs/2010.02804
Autor:
Mager, Manuel, Kann, Katharina
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 task consists of gen
Externí odkaz:
http://arxiv.org/abs/2005.12411
Autor:
Mager, Manuel, Astudillo, Ramon Fernandez, Naseem, Tahira, Sultan, Md Arafat, Lee, Young-Suk, Florian, Radu, Roukos, Salim
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2005.09123