4 research outputs found
Factify 2: A Multimodal Fake News and Satire News Dataset
The internet gives the world an open platform to express their views and
share their stories. While this is very valuable, it makes fake news one of our
society's most pressing problems. Manual fact checking process is time
consuming, which makes it challenging to disprove misleading assertions before
they cause significant harm. This is he driving interest in automatic fact or
claim verification. Some of the existing datasets aim to support development of
automating fact-checking techniques, however, most of them are text based.
Multi-modal fact verification has received relatively scant attention. In this
paper, we provide a multi-modal fact-checking dataset called FACTIFY 2,
improving Factify 1 by using new data sources and adding satire articles.
Factify 2 has 50,000 new data instances. Similar to FACTIFY 1.0, we have three
broad categories - support, no-evidence, and refute, with sub-categories based
on the entailment of visual and textual data. We also provide a BERT and Vison
Transformer based baseline, which acheives 65% F1 score in the test set. The
baseline codes and the dataset will be made available at
https://github.com/surya1701/Factify-2.0.Comment: Defactify@AAAI202
A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods
Opinion and sentiment analysis is a vital task to characterize subjective
information in social media posts. In this paper, we present a comprehensive
experimental evaluation and comparison with six state-of-the-art methods, from
which we have re-implemented one of them. In addition, we investigate different
textual and visual feature embeddings that cover different aspects of the
content, as well as the recently introduced multimodal CLIP embeddings.
Experimental results are presented for two different publicly available
benchmark datasets of tweets and corresponding images. In contrast to the
evaluation methodology of previous work, we introduce a reproducible and fair
evaluation scheme to make results comparable. Finally, we conduct an error
analysis to outline the limitations of the methods and possibilities for the
future work.Comment: Accepted in Workshop on Multi-ModalPre-Training for Multimedia
Understanding (MMPT 2021), co-located with ICMR 202
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning
Deep learning (DL) has emerged as a leading approach in accelerating MR
imaging. It employs deep neural networks to extract knowledge from available
datasets and then applies the trained networks to reconstruct accurate images
from limited measurements. Unlike natural image restoration problems, MR
imaging involves physics-based imaging processes, unique data properties, and
diverse imaging tasks. This domain knowledge needs to be integrated with
data-driven approaches. Our review will introduce the significant challenges
faced by such knowledge-driven DL approaches in the context of fast MR imaging
along with several notable solutions, which include learning neural networks
and addressing different imaging application scenarios. The traits and trends
of these techniques have also been given which have shifted from supervised
learning to semi-supervised learning, and finally, to unsupervised learning
methods. In addition, MR vendors' choices of DL reconstruction have been
provided along with some discussions on open questions and future directions,
which are critical for the reliable imaging systems.Comment: 46 pages, 5figures, 1 tabl
Low-Resource Event Extraction
The last decade has seen the extraordinary evolution of deep learning in natural language processing leading to the rapid deployment of many natural language processing applications. However, the field of event extraction did not witness a parallel success story due to the inherent challenges associated with its scalability. The task itself is much more complex than other NLP tasks due to the dependency among its subtasks. This interlocking system of tasks requires a full adaptation whenever one attempts to scale to another domain or language, which is too expensive to scale to thousands of domains and languages. This dissertation introduces a holistic method for expanding event extraction to other domains and languages within the limited available tools and resources. First, this study focuses on designing neural network architecture that enables the integration of external syntactic and graph features as well as external knowledge bases to enrich the hidden representations of the events. Second, this study presents network architecture and training methods for efficient learning under minimal supervision. Third, we created brand new multilingual corpora for event relation extraction to facilitate the research of event extraction in low-resource languages. We also introduce a language-agnostic method to tackle multilingual event relation extraction. Our extensive experiment shows the effectiveness of these methods which will significantly speed up the advance of the event extraction field. We anticipate that this research will stimulate the growth of the event detection field in unexplored domains and languages, ultimately leading to the expansion of language technologies into a more extensive range of diaspora