9 research outputs found
A Pairwise Dataset for GUI Conversion and Retrieval between Android Phones and Tablets
With the popularity of smartphones and tablets, users have become accustomed
to using different devices for different tasks, such as using their phones to
play games and tablets to watch movies. To conquer the market, one app is often
available on both smartphones and tablets. However, although one app has
similar graphic user interfaces (GUIs) and functionalities on phone and tablet,
current app developers typically start from scratch when developing a
tablet-compatible version of their app, which drives up development costs and
wastes existing design resources. Researchers are attempting to employ deep
learning in automated GUIs development to enhance developers' productivity.
Deep learning models rely heavily on high-quality datasets. There are currently
several publicly accessible GUI page datasets for phones, but none for pairwise
GUIs between phones and tablets. This poses a significant barrier to the
employment of deep learning in automated GUI development. In this paper, we
collect and make public the Papt dataset, which is a pairwise dataset for GUI
conversion and retrieval between Android phones and tablets. The dataset
contains 10,035 phone-tablet GUI page pairs from 5,593 phone-tablet app pairs.
We illustrate the approaches of collecting pairwise data and statistical
analysis of this dataset. We also illustrate the advantages of our dataset
compared to other current datasets. Through preliminary experiments on this
dataset, we analyse the present challenges of utilising deep learning in
automated GUI development and find that our dataset can assist the application
of some deep learning models to tasks involving automatic GUI development.Comment: 10 pages, 9 figure
Turning Flowchart into Dialog: Plan-based Data Augmentation for Low-Resource Flowchart-grounded Troubleshooting Dialogs
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the
instructions of a flowchart to diagnose users' problems in specific domains
(eg., vehicle, laptop), have been gaining research interest in recent years.
However, collecting sufficient dialogues that are naturally grounded on
flowcharts is costly, thus FTD systems are impeded by scarce training data. To
mitigate the data sparsity issue, we propose a plan-based data augmentation
(PlanDA) approach that generates diverse synthetic dialog data at scale by
transforming concise flowchart into dialogues. Specifically, its generative
model employs a variational-base framework with a hierarchical planning
strategy that includes global and local latent planning variables. Experiments
on the FloDial dataset show that synthetic dialogue produced by PlanDA improves
the performance of downstream tasks, including flowchart path retrieval and
response generation, in particular on the Out-of-Flowchart settings. In
addition, further analysis demonstrate the quality of synthetic data generated
by PlanDA in paths that are covered by current sample dialogues and paths that
are not covered
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation
Existing neural methods have shown great potentials towards generating
informative text from structured tabular data as well as maintaining high
content fidelity. However, few of them shed light on generating personalized
expressions, which often requires well-aligned persona-table-text datasets that
are difficult to obtain. To overcome these obstacles, we explore personalized
table-to-text generation under a zero-shot setting, by assuming no well-aligned
persona-table-text triples are required during training. To this end, we
firstly collect a set of unpaired persona information and then propose a
semi-supervised approach with contrastive persona distillation (S2P-CPD) to
generate personalized context. Specifically, tabular data and persona
information are firstly represented as latent variables separately. Then, we
devise a latent space fusion technique to distill persona information into the
table representation. Besides, a contrastive-based discriminator is employed to
guarantee the style consistency between the generated context and its
corresponding persona. Experimental results on two benchmarks demonstrate
S2P-CPD's ability on keeping both content fidelity and personalized
expressions.Comment: Accepted by ICASSP 202
GOOD: Towards Domain Generalized Orientated Object Detection
Oriented object detection has been rapidly developed in the past few years,
but most of these methods assume the training and testing images are under the
same statistical distribution, which is far from reality. In this paper, we
propose the task of domain generalized oriented object detection, which intends
to explore the generalization of oriented object detectors on arbitrary unseen
target domains. Learning domain generalized oriented object detectors is
particularly challenging, as the cross-domain style variation not only
negatively impacts the content representation, but also leads to unreliable
orientation predictions. To address these challenges, we propose a generalized
oriented object detector (GOOD). After style hallucination by the emerging
contrastive language-image pre-training (CLIP), it consists of two key
components, namely, rotation-aware content consistency learning (RAC) and style
consistency learning (SEC). The proposed RAC allows the oriented object
detector to learn stable orientation representation from style-diversified
samples. The proposed SEC further stabilizes the generalization ability of
content representation from different image styles. Extensive experiments on
multiple cross-domain settings show the state-of-the-art performance of GOOD.
Source code will be publicly available.Comment: 8 pages, 6 figure
Probing Product Description Generation via Posterior Distillation
In product description generation (PDG), the user-cared aspect is critical
for the recommendation system, which can not only improve user's experiences
but also obtain more clicks. High-quality customer reviews can be considered as
an ideal source to mine user-cared aspects. However, in reality, a large number
of new products (known as long-tailed commodities) cannot gather sufficient
amount of customer reviews, which brings a big challenge in the product
description generation task. Existing works tend to generate the product
description solely based on item information, i.e., product attributes or title
words, which leads to tedious contents and cannot attract customers
effectively. To tackle this problem, we propose an adaptive posterior network
based on Transformer architecture that can utilize user-cared information from
customer reviews. Specifically, we first extend the self-attentive Transformer
encoder to encode product titles and attributes. Then, we apply an adaptive
posterior distillation module to utilize useful review information, which
integrates user-cared aspects to the generation process. Finally, we apply a
Transformer-based decoding phase with copy mechanism to automatically generate
the product description. Besides, we also collect a large-scare Chinese product
description dataset to support our work and further research in this field.
Experimental results show that our model is superior to traditional generative
models in both automatic indicators and human evaluation
SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations
In today's globalized world, bridging the cultural divide is more critical
than ever for forging meaningful connections. The Socially-Aware Dialogue
Assistant System (SADAS) is our answer to this global challenge, and it's
designed to ensure that conversations between individuals from diverse cultural
backgrounds unfold with respect and understanding. Our system's novel
architecture includes: (1) identifying the categories of norms present in the
dialogue, (2) detecting potential norm violations, (3) evaluating the severity
of these violations, (4) implementing targeted remedies to rectify the
breaches, and (5) articulates the rationale behind these corrective actions. We
employ a series of State-Of-The-Art (SOTA) techniques to build different
modules, and conduct numerous experiments to select the most suitable backbone
model for each of the modules. We also design a human preference experiment to
validate the overall performance of the system. We will open-source our system
(including source code, tools and applications), hoping to advance future
research. A demo video of our system can be found
at:~\url{https://youtu.be/JqetWkfsejk}. We have released our code and software
at:~\url{https://github.com/AnonymousEACLDemo/SADAS}.Comment: 8 pages, 2 figure