3 research outputs found
Co-occurrences using Fasttext embeddings for word similarity tasks in Urdu
Urdu is a widely spoken language in South Asia. Though immoderate literature
exists for the Urdu language still the data isn't enough to naturally process
the language by NLP techniques. Very efficient language models exist for the
English language, a high resource language, but Urdu and other under-resourced
languages have been neglected for a long time. To create efficient language
models for these languages we must have good word embedding models. For Urdu,
we can only find word embeddings trained and developed using the skip-gram
model. In this paper, we have built a corpus for Urdu by scraping and
integrating data from various sources and compiled a vocabulary for the Urdu
language. We also modify fasttext embeddings and N-Grams models to enable
training them on our built corpus. We have used these trained embeddings for a
word similarity task and compared the results with existing techniques
Cache Bypassing for Machine Learning Algorithms
Graphics Processing Units (GPUs) were once used solely for graphical
computation tasks but with the increase in the use of machine learning
applications, the use of GPUs to perform general-purpose computing has
increased in the last few years. GPUs employ a massive amount of threads, that
in turn achieve a high amount of parallelism, to perform tasks. Though GPUs
have a high amount of computation power, they face the problem of cache
contention due to the SIMT model that they use. A solution to this problem is
called "cache bypassing". This paper presents a predictive model that analyzes
the access patterns of various machine learning algorithms and determines
whether certain data should be stored in the cache or not. It presents insights
on how well each model performs on different datasets and also shows how
minimizing the size of each model will affect its performance The performance
of most of the models were found to be around 90% with KNN performing the best
but not with the smallest size. We further increase the features by splitting
the addresses into chunks of 4 bytes. We observe that this increased the
performance of the neural network substantially and increased the accuracy to
99.9% with three neurons
Few Shot Learning for Information Verification
Information verification is quite a challenging task, this is because many
times verifying a claim can require picking pieces of information from multiple
pieces of evidence which can have a hierarchy of complex semantic relations.
Previously a lot of researchers have mainly focused on simply concatenating
multiple evidence sentences to accept or reject claims. These approaches are
limited as evidence can contain hierarchical information and dependencies. In
this research, we aim to verify facts based on evidence selected from a list of
articles taken from Wikipedia. Pretrained language models such as XLNET are
used to generate meaningful representations and graph-based attention and
convolutions are used in such a way that the system requires little additional
training to learn to verify facts