4 citations · 11 across the 8 of their papers we have counts for
9 papers
Domain Adaptation of Foundation LLMs for e-Commerce
Christian Herold, Michael Kozielski, Tala Bazazo +6
We present the e-Llama models: 8 billion and 70 billion parameter large language models that are adapted towards the e-commerce domain. These models are meant as foundation models…
LiLiuM: eBay's Large Language Models for e-commerce
Christian Herold, Michael Kozielski, Leonid Ekimov +3
We introduce the LiLiuM series of large language models (LLMs): 1B, 7B, and 13B parameter models developed 100% in-house to fit eBay's specific needs in the e-commerce domain. This…
Document-Level Language Models for Machine Translation
Frithjof Petrick, Christian Herold, Pavel Petrushkov +2
Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence…
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high res…
Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models. However, cascaded models are known to be prone to error…
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
Yunsu Kim, Petre Petrov, Pavel Petrushkov +2
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to…
Word-based Domain Adaptation for Neural Machine Translation
Shen Yan, Leonard Dahlmann, Pavel Petrushkov +2
In this paper, we empirically investigate applying word-level weights to adapt neural machine translation to e-commerce domains, where small e-commerce datasets and large out-of-do…
Learning from Chunk-based Feedback in Neural Machine Translation
Pavel Petrushkov, Shahram Khadivi, Evgeny Matusov
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a…
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Leonard Dahlmann, Evgeny Matusov, Pavel Petrushkov +1
In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT…