100 research outputs found
RESEARCH ON THE STRATEGY OF INTERNET FINANCE HELPING RURAL REVITALIZATION IN XINJIANG FROM THE PERSPECTIVE OF COGNITIVE PSYCHOLOGY
KAT: A Knowledge Augmented Transformer for Vision-and-Language
The primary focus of recent work with largescale transformers has been on
optimizing the amount of information packed into the model's parameters. In
this work, we ask a different question: Can multimodal transformers leverage
explicit knowledge in their reasoning? Existing, primarily unimodal, methods
have explored approaches under the paradigm of knowledge retrieval followed by
answer prediction, but leave open questions about the quality and relevance of
the retrieved knowledge used, and how the reasoning processes over implicit and
explicit knowledge should be integrated. To address these challenges, we
propose a novel model - Knowledge Augmented Transformer (KAT) - which achieves
a strong state-of-the-art result (+6 points absolute) on the open-domain
multimodal task of OK-VQA. Our approach integrates implicit and explicit
knowledge in an end to end encoder-decoder architecture, while still jointly
reasoning over both knowledge sources during answer generation. An additional
benefit of explicit knowledge integration is seen in improved interpretability
of model predictions in our analysis.Comment: Accepted by NAACL 202
Model Predictive Control for a Grid-interactive Efficiency Thermal-storage-integrated Heat Pump System
Ellipse Fitting Based Approach for Extended Object Tracking
With the increase of sensors’ resolution, traditional object tracking technology, which ignores object’s physical extension, gradually becomes inappropriate. Extended object tracking (EOT) technology is able to obtain more information about the object through jointly estimating both centroid’s dynamic state and physical extension of the object. Random matrix based approach is a promising method for EOT. It uses ellipse/ellipsoid to describe the physical extension of the object. In order to reduce the physical extension estimation error when object maneuvers, the relationship between ellipse/ellipsoid and symmetrical positive definite matrix is analyzed at first. On this basis, ellipse/ellipsoid fitting based approach (EFA) for EOT is proposed based on the measurement model and centroid’s dynamic model of random matrix based EOT approach. Simulation results show that EFA is effective. The physical extension estimation error of EFA is lower than those of random matrix based approaches when object maneuvers. Besides, the estimation error of centroid’s dynamic state of EFA is also lower
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
Abstractive dialogue summarization has long been viewed as an important
standalone task in natural language processing, but no previous work has
explored the possibility of whether abstractive dialogue summarization can also
be used as a means to boost an NLP system's performance on other important
dialogue comprehension tasks. In this paper, we propose a novel type of
dialogue summarization task - STRUctured DiaLoguE Summarization - that can help
pre-trained language models to better understand dialogues and improve their
performance on important dialogue comprehension tasks. We further collect human
annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL
dialogue comprehension modeling framework that integrates STRUDEL into a
graph-neural-network-based dialogue reasoning module over transformer encoder
language models to improve their dialogue comprehension abilities. In our
empirical experiments on two important downstream dialogue comprehension tasks
- dialogue question answering and dialogue response prediction - we show that
our STRUDEL dialogue comprehension model can significantly improve the dialogue
comprehension performance of transformer encoder language models.Comment: EMNLP 202
- …