Towards Accurate and Smart Task-Oriented Dialogue Systems

Dialogue systems such as Apple Siri, Microsoft Cortana are designed to help people in many aspects. In particular, task-oriented dialogue systems (TOD) assist humans in finishing various tasks such as setting alarms and making recommendations. Keeping track of user intention and providing information accurately and smartly with minimum conversational turns is a big challenge. In this first work, I propose a discriminative nearest neighbor intent detection model to accurately identify user intentions and recognize unsupported or unrelated out-of-scope (OOS) user queries with limited training examples. In the second work, I further investigate whether pre-trained Transformers are robust in few-shot intent detection w.r.t. general and relevant OOS examples on our newly constructed and released datasets. In the third work, I propose a few-shot intent detection model through contrastive pre-training and fine-tuning to accurately identify both general and fine-grained user intents. I propose a dual strategy for slot-value predictions on dialog state tracking across multiple domains in the fourth work. Furthermore, since pipeline-based systems require lots of annotations and are hard to scale, the errors are also easy to propagate to different modules. We thus want to build accurate and smart end-to-end TOD based on seq2seq language models to leverage sophisticated NLU and NLG. In the fifth work, I present an end-to-end TOD framework via simple 'database' to handle all the components and add flexibility to update intents and slots dynamically

Transferability of Coarse-Grained Force Field for <i>n</i>CB Liquid Crystal Systems

In this paper, the transferability of the coarse-grained (CG) force field originally developed for the liquid crystal (LC) molecule 5CB (Zhang et al. J. Phys. Chem. B 2012, 116, 2075−2089) was investigated by its homologues 6CB and 8CB molecules. Note that, to construct the 5CB CG force field, we combined the structure-based and thermodynamic quantities-based methods and at the same time attempted to use several fragment molecular systems to derive the CG nonbonded interaction parameters. The resultant 5CB CG force field exhibits a good transferability to some extent. For example, not only the experimental densities, the local packing of atom groups, and the antiparallel arrangements of nearest neighboring molecules, but also the unique LC mesophases as well as the nematic–isotropic phase transition temperatures of 6CB and 8CB were reproduced. Meanwhile, the limitations of this 5CB CG force field were also observed, such as the phase transition from nematic to smectic was postponed to the lower temperature and the resulting smectic phase structure is single-layer-like instead of partially interdigitated bilayer-like as observed in underlying atomistic model. Apparently, more attention should be paid when applying a CG force field to the state point which is quite different from which the force field is explicitly parametrized for. The origin of the above limitations can be potentially traced back to the inherent simplifications and some approximations often adopted in the creation process of CG force field, for example, choosing symmetric CG potentials which do not explicitly include electrostatic interactions and are parametrized by reproducing the target properties of the specific nematic 5CB phase at 300 K and 1 atm, as well as using soft nonbonded potential and excluding torsion barriers. Moreover, although by construction this CG force field could inevitably incorporate both thermodynamic and local structural information on the nematic 5CB phase, the anisotropic diffusion coefficient ratios for different LC phases in both 6CB and 8CB systems are reproduced well. All these findings suggest that the multiproperty parametrization route together with fragment-based method provides a new approach to maximize the possibility to simultaneously reproduce multiple physical properties of a given molecule or related molecules with similar chemical structures at other state points

Tree Biomass Estimation of Chinese fir (<i>Cunninghamia lanceolata</i>) Based on Bayesian Method

<div><p>Chinese fir (<i>Cunninghamia lanceolata</i> (Lamb.) Hook.) is the most important conifer species for timber production with huge distribution area in southern China. Accurate estimation of biomass is required for accounting and monitoring Chinese forest carbon stocking. In the study, allometric equation was used to analyze tree biomass of Chinese fir. The common methods for estimating allometric model have taken the classical approach based on the frequency interpretation of probability. However, many different biotic and abiotic factors introduce variability in Chinese fir biomass model, suggesting that parameters of biomass model are better represented by probability distributions rather than fixed values as classical method. To deal with the problem, Bayesian method was used for estimating Chinese fir biomass model. In the Bayesian framework, two priors were introduced: non-informative priors and informative priors. For informative priors, 32 biomass equations of Chinese fir were collected from published literature in the paper. The parameter distributions from published literature were regarded as prior distributions in Bayesian model for estimating Chinese fir biomass. Therefore, the Bayesian method with informative priors was better than non-informative priors and classical method, which provides a reasonable method for estimating Chinese fir biomass.</p></div

A Hierarchical Bayesian Model to Predict Self-Thinning Line for Chinese Fir in Southern China

<div><p>Self-thinning is a dynamic equilibrium between forest growth and mortality at full site occupancy. Parameters of the self-thinning lines are often confounded by differences across various stand and site conditions. For overcoming the problem of hierarchical and repeated measures, we used hierarchical Bayesian method to estimate the self-thinning line. The results showed that the self-thinning line for Chinese fir (<i>Cunninghamia lanceolata</i> (Lamb.)Hook.) plantations was not sensitive to the initial planting density. The uncertainty of model predictions was mostly due to within-subject variability. The simulation precision of hierarchical Bayesian method was better than that of stochastic frontier function (SFF). Hierarchical Bayesian method provided a reasonable explanation of the impact of other variables (site quality, soil type, aspect, etc.) on self-thinning line, which gave us the posterior distribution of parameters of self-thinning line. The research of self-thinning relationship could be benefit from the use of hierarchical Bayesian method.</p></div

Descriptive statistics of stand variables of Chinese fir stands.

<p>(<i>n</i> = 10 plots).</p

Parameter estimates and 95% credible and confidence intervals of each component biomass model based on Bayesian method and MLS method.

<p>Parameter estimates and 95% credible and confidence intervals of each component biomass model based on Bayesian method and MLS method.</p

Posterior probability density of two parameters for each component biomass model.

<p>The left line is Bayesian method with informative prior, and the right line is Bayesian method with non-informative prior.</p

Correlation between total biomass estimates from summation of each component (AT) and direct regression of total biomass (DT).

<p>Correlation between total biomass estimates from summation of each component (AT) and direct regression of total biomass (DT).</p

Parameter estimates of self-thinning line relationships using SFF method.

<p>Parameter estimates of self-thinning line relationships using SFF method.</p

Scatter plots of predicted Ln-mean-volumes based on model M2 using hierarchical Bayesian method, and SFF method.

<p>Values of 2.5%, median, and 97.5% were obtained with hierarchical Bayesian method.</p