100 research outputs found
Learning Optimal Policy for Simultaneous Machine Translation via Binary Search
Simultaneous machine translation (SiMT) starts to output translation while
reading the source sentence and needs a precise policy to decide when to output
the generated translation. Therefore, the policy determines the number of
source tokens read during the translation of each target token. However, it is
difficult to learn a precise translation policy to achieve good latency-quality
trade-offs, because there is no golden policy corresponding to parallel
sentences as explicit supervision. In this paper, we present a new method for
constructing the optimal policy online via binary search. By employing explicit
supervision, our approach enables the SiMT model to learn the optimal policy,
which can guide the model in completing the translation during inference.
Experiments on four translation tasks show that our method can exceed strong
baselines across all latency scenarios.Comment: Accepted to ACL 2023. 14 pages, 5 figure
Simultaneous Machine Translation with Tailored Reference
Simultaneous machine translation (SiMT) generates translation while reading
the whole source sentence. However, existing SiMT models are typically trained
using the same reference disregarding the varying amounts of available source
information at different latency. Training the model with ground-truth at low
latency may introduce forced anticipations, whereas utilizing reference
consistent with the source word order at high latency results in performance
degradation. Consequently, it is crucial to train the SiMT model with
appropriate reference that avoids forced anticipations during training while
maintaining high quality. In this paper, we propose a novel method that
provides tailored reference for the SiMT models trained at different latency by
rephrasing the ground-truth. Specifically, we introduce the tailor, induced by
reinforcement learning, to modify ground-truth to the tailored reference. The
SiMT model is trained with the tailored reference and jointly optimized with
the tailor to enhance performance. Importantly, our method is applicable to a
wide range of current SiMT approaches. Experiments on three translation tasks
demonstrate that our method achieves state-of-the-art performance in both fixed
and adaptive policies.Comment: Accepted to EMNLP 2023; 15 pages, 8 figure
Non-autoregressive Streaming Transformer for Simultaneous Translation
Simultaneous machine translation (SiMT) models are trained to strike a
balance between latency and translation quality. However, training these models
to achieve high quality while maintaining low latency often leads to a tendency
for aggressive anticipation. We argue that such issue stems from the
autoregressive architecture upon which most existing SiMT models are built. To
address those issues, we propose non-autoregressive streaming Transformer
(NAST) which comprises a unidirectional encoder and a non-autoregressive
decoder with intra-chunk parallelism. We enable NAST to generate the blank
token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and
train it to maximize the non-monotonic latent alignment with an alignment-based
latency loss. Experiments on various SiMT benchmarks demonstrate that NAST
outperforms previous strong autoregressive SiMT baselines.Comment: EMNLP 2023 main conference; Source code is available at
https://github.com/ictnlp/NAS
Study on coal mine macro, meso and micro safety management system
SummaryIn recent years, the coal mine safety production situation in our country improved year by year, but severe accidents still occurred; the accidents caused great economic loss to the national economy. According to statistical analysis, almost all of the coal mine accidents will expose the hidden danger in before, most of the accidents caused due to safety management not reaching the designated position and the hidden danger management does not take any decision in time. Based on the coal mine safety management holes in our country, the coal mine macro, meso and micro safety management system was established in this paper, which includes meaning and conception of the theories of the macro, meso and micro safety management, and also includes the matching hardware equipment, in order to achieve the hidden danger's closed-loop control and dynamic early warning in the process of coal mine production
p38α MAPK regulates proliferation and differentiation of osteoclast progenitors and bone remodeling in an aging-dependent manner.
Bone mass is determined by the balance between bone formation, carried out by mesenchymal stem cell-derived osteoblasts, and bone resorption, carried out by monocyte-derived osteoclasts. Here we investigated the potential roles of p38 MAPKs, which are activated by growth factors and cytokines including RANKL and BMPs, in osteoclastogenesis and bone resorption by ablating p38α MAPK in LysM+monocytes. p38α deficiency promoted monocyte proliferation but regulated monocyte osteoclastic differentiation in a cell-density dependent manner, with proliferating p38α-/- cultures showing increased differentiation. While young mutant mice showed minor increase in bone mass, 6-month-old mutant mice developed osteoporosis, associated with an increase in osteoclastogenesis and bone resorption and an increase in the pool of monocytes. Moreover, monocyte-specific p38α ablation resulted in a decrease in bone formation and the number of bone marrow mesenchymal stem/stromal cells, likely due to decreased expression of PDGF-AA and BMP2. The expression of PDGF-AA and BMP2 was positively regulated by the p38 MAPK-Creb axis in osteoclasts, with the promoters of PDGF-AA and BMP2 having Creb binding sites. These findings uncovered the molecular mechanisms by which p38α MAPK regulates osteoclastogenesis and coordinates osteoclastogenesis and osteoblastogenesis
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