12 citations · 27 across the 7 of their papers we have counts for
21 papers
Some Grammatical Errors are Frequent, Others are Important
Leshem Choshen, Ofir Shifman, Omri Abend
In Grammatical Error Correction, systems are evaluated by the number of errors they correct. However, no one has assessed whether all error types are equally important. We provide…
Fusing finetuned models for better pretraining
Leshem Choshen, Elad Venezian, Noam Slonim +1
Pretrained models are the standard starting point for training. This approach consistently outperforms the use of a random initialization. However, pretraining is a costly endeavou…
Cluster & Tune: Boost Cold Start Performance in Text Classification
Eyal Shnarch, Ariel Gera, Alon Halfon +4
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained model…
ComSum: Commit Messages Summarization and Meaning Preservation
Leshem Choshen, Idan Amit
We present ComSum, a data set of 7 million commit messages for text summarization. When documenting commits, software code changes, both a message and its summary are posted. We ga…
Part of Speech and Universal Dependency effects on English Arabic Machine Translation
Ofek Rafaeli, Omri Abend, Leshem Choshen +1
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabi…
SERRANT: a syntactic classifier for English Grammatical Error Types
Leshem Choshen, Matanel Oren, Dmitry Nikolaev +1
SERRANT is a system and code for automatic classification of English grammatical errors that combines SErCl and ERRANT. SERRANT uses ERRANT's annotations when they are informative…