7 research outputs found
Direct Neural Machine Translation with Task-level Mixture of Experts models
Direct neural machine translation (direct NMT) is a type of NMT system that
translates text between two non-English languages. Direct NMT systems often
face limitations due to the scarcity of parallel data between non-English
language pairs. Several approaches have been proposed to address this
limitation, such as multilingual NMT and pivot NMT (translation between two
languages via English). Task-level Mixture of expert models (Task-level MoE),
an inference-efficient variation of Transformer-based models, has shown
promising NMT performance for a large number of language pairs. In Task-level
MoE, different language groups can use different routing strategies to optimize
cross-lingual learning and inference speed. In this work, we examine Task-level
MoE's applicability in direct NMT and propose a series of high-performing
training and evaluation configurations, through which Task-level MoE-based
direct NMT systems outperform bilingual and pivot-based models for a large
number of low and high-resource direct pairs, and translation directions. Our
Task-level MoE with 16 experts outperforms bilingual NMT, Pivot NMT models for
7 language pairs, while pivot-based models still performed better in 9 pairs
and directions
One pot non-emulsion based hydrothermal synthesis of urchin-shaped hydroxy sodalite using waste coal fly ash
Urchin-shaped hydroxy sodalite particles have been synthesized by one pot non-emulsion based hydrothermal conversion of coal fly ash at 100 °C for 96 h in the absence of any surfactants. Crystallization behavior of the particles has been studied by X-ray diffraction, and the characteristic vibration bands of hydroxy sodalite were confirmed by FTIR spectroscopy. BET surface area of the particles are found to be 193, 197, 82 and 44 m2 g-1 for the reaction time of 24 h, 48 h, 72 h and 96 h, respectively. FESEM images revealed that the thread-ball-like particles are converted into urchin-shaped hydroxy sodalite with increasing reaction time from 24 h to 96 h at 100 °C. The elemental analysis of the product studied by EDX indicates the Si/Al and Na/Al ratios of 1.28 and 1.03, respectively which is close to the stoichiometric composition of hydroxy sodalite
Quality Control at Your Fingertips: Quality-Aware Translation Models
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy
for neural machine translation (NMT) models. The underlying assumption is that
model probability correlates well with human judgment, with better translations
being more likely. However, research has shown that this assumption does not
always hold, and decoding strategies which directly optimize a utility
function, like Minimum Bayes Risk (MBR) or Quality-Aware decoding can
significantly improve translation quality over standard MAP decoding. The main
disadvantage of these methods is that they require an additional model to
predict the utility, and additional steps during decoding, which makes the
entire process computationally demanding. In this paper, we propose to make the
NMT models themselves quality-aware by training them to estimate the quality of
their own output. During decoding, we can use the model's own quality estimates
to guide the generation process and produce the highest-quality translations
possible. We demonstrate that the model can self-evaluate its own output during
translation, eliminating the need for a separate quality estimation model.
Moreover, we show that using this quality signal as a prompt during MAP
decoding can significantly improve translation quality. When using the internal
quality estimate to prune the hypothesis space during MBR decoding, we can not
only further improve translation quality, but also reduce inference speed by
two orders of magnitude
Blue carbon stock of the Bangladesh Sundarban mangroves: what could be the scenario after a century?
The total blue carbon stock of the Bangladesh Sundarban mangroves was evaluated and the probable future status after a century was predicted based on the recent trend of changes in the last 30 years and implementing a hybrid model of Markov Chain and Cellular automata. At present 36.24 Tg C and 54.95 Tg C are stored in the above-ground and below-ground compartments respectively resulting in total blue carbon stock of 91.19 Tg C. According to the prediction 15.88 Tg C would be lost from this region by the year 2115. The low saline species composition classes dominated mainly by Heritiera spp. accounts for the major portion of the carbon sock at present (45.60 Tg C), while the highly saline regions stores only 14.90 Tg C. The prediction shows that after a hundred years almost 22.42 Tg C would be lost from the low saline regions accompanied by an increase of 8.20 Tg C in the high saline regions dominated mainly by Excoecaria sp. and Avicennia spp. The net carbon loss would be due to both mangrove area loss (~ 510 km2) and change in species composition leading to 58.28 Tg of potential CO2 emission within the year 2115