55 research outputs found
Learning to Select from Multiple Options
Many NLP tasks can be regarded as a selection problem from a set of options,
such as classification tasks, multi-choice question answering, etc. Textual
entailment (TE) has been shown as the state-of-the-art (SOTA) approach to
dealing with those selection problems. TE treats input texts as premises (P),
options as hypotheses (H), then handles the selection problem by modeling (P,
H) pairwise. Two limitations: first, the pairwise modeling is unaware of other
options, which is less intuitive since humans often determine the best options
by comparing competing candidates; second, the inference process of pairwise TE
is time-consuming, especially when the option space is large. To deal with the
two issues, this work first proposes a contextualized TE model (Context-TE) by
appending other k options as the context of the current (P, H) modeling.
Context-TE is able to learn more reliable decision for the H since it considers
various context. Second, we speed up Context-TE by coming up with Parallel-TE,
which learns the decisions of multiple options simultaneously. Parallel-TE
significantly improves the inference speed while keeping comparable performance
with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity
typing, intent detection and multi-choice QA) that are typical selection
problems with different sizes of options. Experiments show our models set new
SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k
times in inference. Our code is publicly available at
https://github.com/jiangshdd/LearningToSelect.Comment: Accepted by AAAI 202
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm
In intent detection tasks, leveraging meaningful semantic information from
intent labels can be particularly beneficial for few-shot scenarios. However,
existing few-shot intent detection methods either ignore the intent labels,
(e.g. treating intents as indices) or do not fully utilize this information
(e.g. only using part of the intent labels). In this work, we present an
end-to-end One-to-All system that enables the comparison of an input utterance
with all label candidates. The system can then fully utilize label semantics in
this way. Experiments on three few-shot intent detection tasks demonstrate that
One-to-All is especially effective when the training resource is extremely
scarce, achieving state-of-the-art performance in 1-, 3- and 5-shot settings.
Moreover, we present a novel pretraining strategy for our model that utilizes
indirect supervision from paraphrasing, enabling zero-shot cross-domain
generalization on intent detection tasks. Our code is at
https://github.com/jiangshdd/AllLablesTogether.Comment: Accepted by IJCNLP-AACL 202
Ultrafast X-ray scattering offers a structural view of excited-state charge transfer
Intramolecular charge transfer and the associated changes in molecular structure in N,N'-dimethylpiperazine are tracked using femtosecond gas-phase X-ray scattering. The molecules are optically excited to the 3p state at 200 nm. Following rapid relaxation to the 3s state, distinct charge-localized and charge-delocalized species related by charge transfer are observed. The experiment determines the molecular structure of the two species, with the redistribution of electron density accounted for by a scattering correction factor. The initially dominant charge-localized state has a weakened carbon-carbon bond and reorients one methyl group compared with the ground state. Subsequent charge transfer to the charge-delocalized state elongates the carbon-carbon bond further, creating an extended 1.634 Ã… bond, and also reorients the second methyl group. At the same time, the bond lengths between the nitrogen and the ring-carbon atoms contract from an average of 1.505 to 1.465 Ã…. The experiment determines the overall charge transfer time constant for approaching the equilibrium between charge-localized and charge-delocalized species to 3.0 ps
Determining Orientations of Optical Transition Dipole Moments Using Ultrafast X-ray Scattering
Identification
of the initially prepared, optically active state
remains a challenging problem in many studies of ultrafast photoinduced
processes. We show that the initially excited electronic state can
be determined using the anisotropic component of ultrafast time-resolved
X-ray scattering signals. The concept is demonstrated using the time-dependent
X-ray scattering of <i>N</i>-methyl morpholine in the gas
phase upon excitation by a 200 nm linearly polarized optical pulse.
Analysis of the angular dependence of the scattering signal near time
zero renders the orientation of the transition dipole moment in the
molecular frame and identifies the initially excited state as the
3p<sub><i>z</i></sub> Rydberg state, thus bypassing the
need for further experimental studies to determine the starting point
of the photoinduced dynamics and clarifying inconsistent computational
results
A Novel Strategy to Construct Yeast Saccharomyces cerevisiae Strains for Very High Gravity Fermentation
Very high gravity (VHG) fermentation is aimed to considerably increase both the fermentation rate and the ethanol concentration, thereby reducing capital costs and the risk of bacterial contamination. This process results in critical issues, such as adverse stress factors (ie., osmotic pressure and ethanol inhibition) and high concentrations of metabolic byproducts which are difficult to overcome by a single breeding method. In the present paper, a novel strategy that combines metabolic engineering and genome shuffling to circumvent these limitations and improve the bioethanol production performance of Saccharomyces cerevisiae strains under VHG conditions was developed. First, in strain Z5, which performed better than other widely used industrial strains, the gene GPD2 encoding glycerol 3-phosphate dehydrogenase was deleted, resulting in a mutant (Z5ΔGPD2) with a lower glycerol yield and poor ethanol productivity. Second, strain Z5ΔGPD2 was subjected to three rounds of genome shuffling to improve its VHG fermentation performance, and the best performing strain SZ3-1 was obtained. Results showed that strain SZ3-1 not only produced less glycerol, but also increased the ethanol yield by up to 8% compared with the parent strain Z5. Further analysis suggested that the improved ethanol yield in strain SZ3-1 was mainly contributed by the enhanced ethanol tolerance of the strain. The differences in ethanol tolerance between strains Z5 and SZ3-1 were closely associated with the cell membrane fatty acid compositions and intracellular trehalose concentrations. Finally, genome rearrangements in the optimized strain were confirmed by karyotype analysis. Hence, a combination of genome shuffling and metabolic engineering is an efficient approach for the rapid improvement of yeast strains for desirable industrial phenotypes
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