Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines

End-to-end neural networks have achieved promising performances in natural language generation (NLG). However, they are treated as black boxes and lack interpretability. To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input dialogue act (DA) into an utterance. HRM consists of a renderer set and a mode switcher. The renderer set contains multiple decoders that vary in both structure and functionality. For every generation step, the mode switcher selects an appropriate decoder from the renderer set to generate an item (a word or a phrase). To verify the effectiveness of our method, we have conducted extensive experiments on 5 benchmark datasets. In terms of automatic metrics (e.g., BLEU), our model is competitive with the current state-of-the-art method. The qualitative analysis shows that our model can interpret the rendering process of neural generators well. Human evaluation also confirms the interpretability of our proposed approach.Comment: Accepted as a conference paper at AAAI 202

OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding

Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, event argument extraction, and event relation extraction. To facilitate related research and application, we present an event understanding toolkit OmniEvent, which features three desiderata: (1) Comprehensive. OmniEvent supports mainstream modeling paradigms of all the event understanding tasks and the processing of 15 widely-used English and Chinese datasets. (2) Fair. OmniEvent carefully handles the inconspicuous evaluation pitfalls reported in Peng et al. (2023), which ensures fair comparisons between different models. (3) Easy-to-use. OmniEvent is designed to be easily used by users with varying needs. We provide off-the-shelf models that can be directly deployed as web services. The modular framework also enables users to easily implement and evaluate new event understanding models with OmniEvent. The toolkit (https://github.com/THU-KEG/OmniEvent) is publicly released along with the demonstration website and video (https://omnievent.xlore.cn/)

Improved Data Transmission Scheme of Network Coding Based on Access Point Optimization in VANET

VANET is a hot spot of intelligent transportation researches. For vehicle users, the file sharing and content distribution through roadside access points (AP) as well as the vehicular ad hoc networks (VANET) have been an important complement to that cellular network. So the AP deployment is one of the key issues to improve the communication performance of VANET. In this paper, an access point optimization method is proposed based on particle swarm optimization algorithm. The transmission performances of the routing protocol with random linear network coding before and after the access point optimization are analyzed. The simulation results show the optimization model greatly affects the VANET transmission performances based on network coding, and it can enhance the delivery rate by 25% and 14% and reduce the average delay of transmission by 38% and 33%

Ionic Liquid-Modulated Synthesis of Porous Worm-Like Gold with Strong SERS Response and Superior Catalytic Activities

Porous gold with well-defined shape and size have aroused extensive research enthusiasm due to their prominent properties in various applications. However, it is still a great challenge to explore a simple, green, and low-cost route to fabricate porous gold with a “clean” surface. In this work, porous worm-like Au has been easily synthesized in a one-step procedure from aqueous solution at room temperature under the action of ionic liquid tetrapropylammonium glycine ([N3333][Gly]). It is shown that the as-prepared porous worm-like Au has the length from 0.3 to 0.6 μm and the width of approximately 100–150 nm, and it is composed of lots of small nanoparticles about 6–12 nm in diameter. With rhodamine 6G (R6G) as a probe molecule, porous worm-like Au displays remarkable surface enhanced Raman scattering (SERS) sensitivity (detection limit is lower than 10−13 M), and extremely high reproducibility (average relative standard deviations is less than 2%). At the same time, owing to significantly high specific surface area, various pore sizes and plenty of crystal defects, porous worm-like Au also exhibits excellent catalytic performance in the reduction of nitroaromatics, such as p-nitrophenol and p-nitroaniline, which can be completely converted within only 100 s and 150 s, respectively. It is expected that the as-prepared porous worm-like Au with porous and self-supported structures will also present the encouraging advances in electrocatalysis, sensing, and many others

Sequential noise compensation by sequential Monte Carlo method

We present a sequential Monte Carlo method applied to additive noise compensation for robust speech recognition in time-varying noise. The method generates a set of samples according to the prior distribution given by clean speech models and noise prior evolved from previous estimation. An explicit model representing noise effects on speech features is used, so that an extended Kalman filter is constructed for each sample, generating the updated continuous state estimate as the estimation of the noise parameter, and prediction likelihood for weighting each sample. Minimum mean square error (MMSE) inference of the time-varying noise parameter is carried out over these samples by fusion the estimation of samples according to their weights. A residual resampling selection step and a Metropolis-Hastings smoothing step are used to improve calculation e#ciency. Experiments were conducted on speech recognition in simulated non-stationary noises, where noise power changed artificially, and highly non-stationary Machinegun noise. In all the experiments carried out, we observed that the method can have significant recognition performance improvement, over that achieved by noise compensation with stationary noise assumption

Time-Varying Noise Estimation for Speech Enhancement and Recognition Using Sequential Monte Carlo Method

We present a method for sequentially estimating time-varying noise parameters. Noise parameters are sequences of time-varying mean vectors representing the noise power in the log-spectral domain. The proposed sequential Monte Carlo method generates a set of particles in compliance with the prior distribution given by clean speech models. The noise parameters in this model evolve according to random walk functions and the model uses extended Kalman filters to update the weight of each particle as a function of observed noisy speech signals, speech model parameters, and the evolved noise parameters in each particle. Finally, the updated noise parameter is obtained by means of minimum mean square error (MMSE) estimation on these particles. For efficient computations, the residual resampling and Metropolis-Hastings smoothing are used. The proposed sequential estimation method is applied to noisy speech recognition and speech enhancement under strongly time-varying noise conditions. In both scenarios, this method outperforms some alternative methods.</p