161 research outputs found
AWESOME: GPU Memory-constrained Long Document Summarization using Memory Mechanism and Global Salient Content
Long document summarization systems are critical for domains with lengthy and
jargonladen text, yet they present significant challenges to researchers and
developers with limited computing resources. Existing solutions mainly focus on
efficient attentions or divide-and-conquer strategies. The former reduces
theoretical time complexity, but is still memory-heavy. The latter methods
sacrifice global context, leading to uninformative and incoherent summaries.
This work aims to leverage the memory-efficient nature of divide-and-conquer
methods while preserving global context. Concretely, our framework AWESOME uses
two novel mechanisms: (1) External memory mechanisms track previously encoded
document segments and their corresponding summaries, to enhance global document
understanding and summary coherence. (2) Global salient content is further
identified beforehand to augment each document segment to support its
summarization. Extensive experiments on diverse genres of text, including
government reports, transcripts, scientific papers, and novels, show that
AWESOME produces summaries with improved informativeness, faithfulness, and
coherence than competitive baselines on longer documents, while having a
similar or smaller GPU memory footprint
Time-aware Prompting for Text Generation
In this paper, we study the effects of incorporating timestamps, such as
document creation dates, into generation systems. Two types of time-aware
prompts are investigated: (1) textual prompts that encode document timestamps
in natural language sentences; and (2) linear prompts that convert timestamps
into continuous vectors. To explore extrapolation to future data points, we
further introduce a new data-to-text generation dataset, TempWikiBio,
containing more than 4 millions of chronologically ordered revisions of
biographical articles from English Wikipedia, each paired with structured
personal profiles. Through data-to-text generation on TempWikiBio, text-to-text
generation on the content transfer dataset, and summarization on XSum, we show
that linear prompts on encoder and textual prompts improve the generation
quality on all datasets. Despite having less performance drop when testing on
data drawn from a later time, linear prompts focus more on non-temporal
information and are less sensitive to the given timestamps, according to human
evaluations and sensitivity analyses. Meanwhile, textual prompts establish the
association between the given timestamps and the output dates, yielding more
factual temporal information in the output.Comment: EMNLP 2022 Findings (short paper
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
Motion representation plays a vital role in human action recognition in
videos. In this study, we introduce a novel compact motion representation for
video action recognition, named Optical Flow guided Feature (OFF), which
enables the network to distill temporal information through a fast and robust
approach. The OFF is derived from the definition of optical flow and is
orthogonal to the optical flow. The derivation also provides theoretical
support for using the difference between two frames. By directly calculating
pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be
embedded in any existing CNN based video action recognition framework with only
a slight additional cost. It enables the CNN to extract spatiotemporal
information, especially the temporal information between frames simultaneously.
This simple but powerful idea is validated by experimental results. The network
with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on
UCF-101, which is comparable with the result obtained by two streams (RGB and
optical flow), but is 15 times faster in speed. Experimental results also show
that OFF is complementary to other motion modalities such as optical flow. When
the proposed method is plugged into the state-of-the-art video action
recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51
respectively. The code for this project is available at
https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.Comment: CVPR 2018. code available at
https://github.com/kevin-ssy/Optical-Flow-Guided-Featur
Trust-aware motion planning for human-robot collaboration under distribution temporal logic specifications
Indirect Effects of Parenting Style on the Relationship between Maternal Personality and Children’s Creativity
Mothers are important persons in the development of creativity in young children. The aim of this study was to examine whether and how mothers influence the creativity of their children. We investigated maternal personality and parenting style through parents’ reports, while children’s creativity of 88 Chinese kindergarten children (age M = 5.50, SD = 0.567) was rated by Torrance TCAM. There was an indirect effect of authoritarian parenting in the relation between maternal neuroticism and children’s creativity in terms of originality. Neurotic mothers may tend to be more authoritarian and in turn, reduce their children’s creativity. Further, parenting style related to not setting any guidelines at all (or NGA) may have indirect effects on the relation between maternal conscientiousness and openness toward children’s creativity. The opposite direction between Chinese parents’ preferential parenting and creativity encouragement parenting was found
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