Abstract. To tackle the problem of severe air pollution, China has implemented active
clean air policies in recent years. As a consequence, the emissions of major
air pollutants have decreased and the air quality has substantially improved.
Here, we quantified China's anthropogenic emission trends from 2010 to 2017
and identified the major driving forces of these trends by using a
combination of bottom-up emission inventory and index decomposition analysis
(IDA) approaches. The relative change rates of China's anthropogenic
emissions during 2010–2017 are estimated as follows: −62 % for
SO2, −17 % for NOx, +11 % for nonmethane
volatile organic compounds (NMVOCs), +1 % for NH3, −27 %
for CO, −38 % for PM10, −35 % for PM2.5, −27 %
for BC, −35 % for OC, and +16 % for CO2. The IDA results
suggest that emission control measures are the main drivers of this
reduction, in which the pollution controls on power plants and industries are
the most effective mitigation measures. The emission reduction rates markedly
accelerated after the year 2013, confirming the effectiveness of China's
Clean Air Action that was implemented since 2013. We estimated that during
2013–2017, China's anthropogenic emissions decreased by 59 % for
SO2, 21 % for NOx, 23 % for CO, 36 % for
PM10, 33 % for PM2.5, 28 % for BC, and 32 % for OC.
NMVOC emissions increased and NH3 emissions remained stable during
2010–2017, representing the absence of effective mitigation measures for
NMVOCs and NH3 in current policies. The relative contributions of
different sectors to emissions have significantly changed after several
years' implementation of clean air policies, indicating that it is paramount
to introduce new policies to enable further emission reductions in the
future.
Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue systems. Joint learning has shown to be effective in SLU given that slot tags and intents are supposed to share knowledge with each other. However, most existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. In this work, we propose a novel self-attentive model with gate mechanism to fully utilize the semantic correlation between slot and intent. Our model first obtains intent-augmented embeddings based on neural network with self-attention mechanism. And then the intent semantic representation is utilized as the gate for labelling slot tags. The objectives of both tasks are optimized simultaneously via joint learning in an end-to-end way. We conduct experiment on popular benchmark ATIS. The results show that our model achieves state-of-the-art and outperforms other popular methods by a large margin in terms of both intent detection error rate and slot filling F1-score. This paper gives a new perspective for research on SLU.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.