Search Results for author:
Found 13 papers, 8 papers with code
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers
In this paper, we explore the effect of quantization on the robustness of Transformer-based models.
Views Are My Own, But Also Yours: Benchmarking Theory of Mind using Common Ground
Evaluating the theory of mind (ToM) capabilities of language models (LMs) has recently received much attention.
Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations
An important part of cognitive state is the common ground, which is the content the speaker believes, and the speaker believes the audience believes, and so on.
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection
Moreover, this is the first time that the concept of multi-task learning has been introduced to the field of Sperm Morphology Analysis (SMA).
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry
Persian Poetry has consistently expressed its philosophy, wisdom, speech, and rationale on the basis of its couplets, making it an enigmatic language on its own to both native and non-native speakers.
Improving Question Answering Performance Using Knowledge Distillation and Active Learning
Contemporary question answering (QA) systems, including transformer-based architectures, suffer from increasing computational and model complexity which render them inefficient for real-world applications with limited resources.
A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA
The explosive growth of textual data on the web and the problem of obtaining desired information through this enormous volume of data has led to a dramatic increase in demand for developing automatic text summarization systems.
PGST: a Polyglot Gender Style Transfer method
Since different approaches are introduced in our research, we determine a trade-off value for evaluating different models' success in faking our gender identification model with transferred text.
DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus
To best of our knowledge, we do not merely suffer from lack of well-annotated Persian sentiment corpus, but also a novel model to classify the Persian opinions in terms of both multiple and binary classification.
LexiPers: An ontology based sentiment lexicon for Persian
The results show an acceptable performance in terms of accuracy and F-measure in the generated sentiment lexicon.
Genetic Neural Architecture Search for automatic assessment of human sperm images
Every individual of the genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome), and its fitness is calculated by a novel proposed method named GeNAS-WF especially designed for noisy, low resolution, and imbalanced datasets.
SentiPers: A Sentiment Analysis Corpus for Persian
To the best of our knowledge, SentiPers is a unique sentiment corpus with such a rich annotation in three different levels including document-level, sentence-level, and entity/aspect-level for Persian.
Towards Unsupervised Learning of Temporal Relations between Events
We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events.
