46 research outputs found
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation
Practical dialog systems need to deal with various knowledge sources, noisy
user expressions, and the shortage of annotated data. To better solve the above
problems, we propose CGoDial, new challenging and comprehensive Chinese
benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763
dialog sessions and 574,949 dialog turns totally, covering three datasets with
different knowledge sources: 1) a slot-based dialog (SBD) dataset with
table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed
knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed
knowledge. To bridge the gap between academic benchmarks and spoken dialog
scenarios, we either collect data from real conversations or add spoken
features to existing datasets via crowd-sourcing. The proposed experimental
settings include the combinations of training with either the entire training
set or a few-shot training set, and testing with either the standard test set
or a hard test subset, which can assess model capabilities in terms of general
prediction, fast adaptability and reliable robustness.Comment: EMNLP 202
VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue
Visually-grounded dialog systems, which integrate multiple modes of
communication such as text and visual inputs, have become an increasingly
popular area of investigation. However, the absence of a standardized
evaluation framework poses a challenge in assessing the development of this
field. To this end, we propose \textbf{VDialogUE}, a \textbf{V}isually-grounded
\textbf{Dialog}ue benchmark for \textbf{U}nified \textbf{E}valuation. It
defines five core multi-modal dialogue tasks and covers six datasets.
Furthermore, in order to provide a comprehensive assessment of the model's
performance across all tasks, we developed a novel evaluation metric called
VDscore, which is based on the Analytic Hierarchy Process~(AHP) method.
Additionally, we present a straightforward yet efficient baseline model, named
\textbf{VISIT}~(\textbf{VIS}ually-grounded d\textbf{I}alog
\textbf{T}ransformer), to promote the advancement of general multi-modal
dialogue systems. It progressively builds its multi-modal foundation and
dialogue capability via a two-stage pre-training strategy.
We believe that the VDialogUE benchmark, along with the evaluation scripts
and our baseline models, will accelerate the development of visually-grounded
dialog systems and lead to the development of more sophisticated and effective
pre-trained models
Multideep Feature Fusion Algorithm for Clothing Style Recognition
In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network-based methods
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings. For reproducibility, we release the code and data at https://github.com/siat-nlp/GALAXY
Highly-parallelized simulation of a pixelated LArTPC on a GPU
The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype