5 research outputs found
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture
Supporting the current trend in the AI community, we present the AI Journey
2021 Challenge called Fusion Brain, the first competition which is targeted to
make the universal architecture which could process different modalities (in
this case, images, texts, and code) and solve multiple tasks for vision and
language. The Fusion Brain Challenge combines the following specific tasks:
Code2code Translation, Handwritten Text recognition, Zero-shot Object
Detection, and Visual Question Answering. We have created datasets for each
task to test the participants' submissions on it. Moreover, we have collected
and made publicly available a new handwritten dataset in both English and
Russian, which consists of 94,128 pairs of images and texts. We also propose a
multimodal and multitask architecture - a baseline solution, in the center of
which is a frozen foundation model and which has been trained in Fusion mode
along with Single-task mode. The proposed Fusion approach proves to be
competitive and more energy-efficient compared to the task-specific one
Democratizing Neural Machine Translation with OPUS-MT
This paper presents the OPUS ecosystem with a focus on the development of
open machine translation models and tools, and their integration into end-user
applications, development platforms and professional workflows. We discuss our
on-going mission of increasing language coverage and translation quality, and
also describe on-going work on the development of modular translation models
and speed-optimized compact solutions for real-time translation on regular
desktops and small devices
Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian
We measure the intensity of diachronic semantic shifts in adjectives in English, Norwegian and Russian across 5 decades. This is done in order to test the hypothesis that evaluative adjectives are more prone to temporal semantic change. To this end, 6 different methods of quantifying semantic change are used. Frequency-controlled experimental results show that, depending on the particular method, evaluative adjectives either do not differ from other types of adjectives in terms of semantic change or appear to actually be less prone to shifting (particularly, to ‘jitter’-type shifting). Thus, in spite of many well-known examples of semantically changing evaluative adjectives (like ‘terrific’ or ‘incredible’), it seems that such cases are not specific to this particular type of words
Democratizing neural machine translation with OPUS-MT
This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our ongoing mission of increasing language coverage and translation quality, and also describe work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.Peer reviewe