6 research outputs found
ConvGenVisMo: Evaluation of Conversational Generative Vision Models
Conversational generative vision models (CGVMs) like Visual ChatGPT (Wu et
al., 2023) have recently emerged from the synthesis of computer vision and
natural language processing techniques. These models enable more natural and
interactive communication between humans and machines, because they can
understand verbal inputs from users and generate responses in natural language
along with visual outputs. To make informed decisions about the usage and
deployment of these models, it is important to analyze their performance
through a suitable evaluation framework on realistic datasets. In this paper,
we present ConvGenVisMo, a framework for the novel task of evaluating CGVMs.
ConvGenVisMo introduces a new benchmark evaluation dataset for this task, and
also provides a suite of existing and new automated evaluation metrics to
evaluate the outputs. All ConvGenVisMo assets, including the dataset and the
evaluation code, will be made available publicly on GitHub
Text Simplification of Scientific Texts for Non-Expert Readers
Reading levels are highly individual and can depend on a text's language, a
person's cognitive abilities, or knowledge on a topic. Text simplification is
the task of rephrasing a text to better cater to the abilities of a specific
target reader group. Simplification of scientific abstracts helps non-experts
to access the core information by bypassing formulations that require domain or
expert knowledge. This is especially relevant for, e.g., cancer patients
reading about novel treatment options. The SimpleText lab hosts the
simplification of scientific abstracts for non-experts (Task 3) to advance this
field. We contribute three runs employing out-of-the-box summarization models
(two based on T5, one based on PEGASUS) and one run using ChatGPT with complex
phrase identification.Comment: Paper accepted at SimpleText@CLEF'23, 12 pages, 1 Figure, 4 Table
Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
Human personality is significantly represented by those words which he/she
uses in his/her speech or writing. As a consequence of spreading the
information infrastructures (specifically the Internet and social media), human
communications have reformed notably from face to face communication.
Generally, Automatic Personality Prediction (or Perception) (APP) is the
automated forecasting of the personality on different types of human
generated/exchanged contents (like text, speech, image, video, etc.). The major
objective of this study is to enhance the accuracy of APP from the text. To
this end, we suggest five new APP methods including term frequency
vector-based, ontology-based, enriched ontology-based, latent semantic analysis
(LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the
base ones, contribute to each other to enhance the APP accuracy through
ensemble modeling (stacking) based on a hierarchical attention network (HAN) as
the meta-model. The results show that ensemble modeling enhances the accuracy
of APP
A Model to Measure the Spread Power of Rumors
Nowadays, a significant portion of daily interacted posts in social media are
infected by rumors. This study investigates the problem of rumor analysis in
different areas from other researches. It tackles the unaddressed problem
related to calculating the Spread Power of Rumor (SPR) for the first time and
seeks to examine the spread power as the function of multi-contextual features.
For this purpose, the theory of Allport and Postman will be adopted. In which
it claims that there are two key factors determinant to the spread power of
rumors, namely importance and ambiguity. The proposed Rumor Spread Power
Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach,
which entails contextual features to compute the spread power of the rumors in
two categories: False Rumor (FR) and True Rumor (TR). Totally 51 contextual
features are introduced to measure SPR and their impact on classification are
investigated, then 42 features in two categories "importance" (28 features) and
"ambiguity" (14 features) are selected to compute SPR. The proposed RSPMM is
verified on two labelled datasets, which are collected from Twitter and
Telegram. The results show that (i) the proposed new features are effective and
efficient to discriminate between FRs and TRs. (ii) the proposed RSPMM approach
focused only on contextual features while existing techniques are based on
Structure and Content features, but RSPMM achieves considerably outstanding
results (F-measure=83%). (iii) The result of T-Test shows that SPR criteria can
significantly distinguish between FR and TR, besides it can be useful as a new
method to verify the trueness of rumors