Search Results for author:
Found 11 papers, 6 papers with code
ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian
As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort.
Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking
Automated fact-checking is an important task because determining the accurate status of a proposed claim within the vast amount of information available online is a critical challenge.
Consistency Training by Synthetic Question Generation for Conversational Question Answering
In our novel model-agnostic approach, referred to as CoTaH (Consistency-Trained augmented History), we augment the historical information with synthetic questions and subsequently employ consistency training to train a model that utilizes both real and augmented historical data to implicitly make the reasoning robust to irrelevant history.
Multi-BERT: Leveraging Adapters and Prompt Tuning for Low-Resource Multi-Domain Adaptation
These challenges are also visible in the Persian name entity recognition (NER) settings.
FaBERT: Pre-training BERT on Persian Blogs
We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts.
PCoQA: Persian Conversational Question Answering Dataset
Our models include baseline models and pre-trained models, which are leveraged to boost the performance of the model.
KhabarChin: Automatic Detection of Important News in the Persian Language
This paper introduces the detection of important news, in a previously unexplored area, and presents a new benchmarking dataset (Khabarchin) for detecting important news in the Persian language.
Persian Natural Language Inference: A Meta-learning approach
We also examine the effect of finding appropriate initial parameters using zero-shot evaluation and CCA similarity.
Gransformer: Transformer-based Graph Generation
This measure of familiarity is based on message passing algorithms and contains structural information about the graph.
Supervised Fuzzy Partitioning
Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications.
Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm.
