3 citations · 6 across the 6 of their papers we have counts for
15 papers
BERTGEN: Multi-task Generation through BERT
Faidon Mitzalis, Ozan Caglayan, Pranava Madhyastha +1
We present BERTGEN, a novel generative, decoder-only model which extends BERT by fusing multimodal and multilingual pretrained models VL-BERT and M-BERT, respectively. BERTGEN is a…
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
Julia Ive, Andy Mingren Li, Yishu Miao +3
This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation…
Cross-lingual Visual Pre-training for Multimodal Machine Translation
Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac +4
Pre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-tra…
MSVD-Turkish: A Comprehensive Multimodal Dataset for Integrated Vision and Language Research in Turkish
Begum Citamak, Ozan Caglayan, Menekse Kuyu +4
Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sent…
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale
Ozan Caglayan, Pranava Madhyastha, Lucia Specia
Automatic evaluation of language generation systems is a well-studied problem in Natural Language Processing. While novel metrics are proposed every year, a few popular metrics rem…
Simultaneous Machine Translation with Visual Context
Ozan Caglayan, Julia Ive, Veneta Haralampieva +3
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation…