9 citations · 31 across the 10 of their papers we have counts for
11 papers · 1 filter
Studying the Role of Input-Neighbor Overlap in Retrieval-Augmented Language Models Training Efficiency
Ehsan Doostmohammadi, Marco Kuhlmann
Retrieval-augmented language models have demonstrated performance comparable to much larger models while requiring fewer computational resources. The effectiveness of these models…
How Reliable Are Automatic Evaluation Methods for Instruction-Tuned LLMs?
Ehsan Doostmohammadi, Oskar Holmström, Marco Kuhlmann
Work on instruction-tuned Large Language Models (LLMs) has used automatic methods based on text overlap and LLM judgments as cost-effective alternatives to human evaluation. In thi…
Surface-Based Retrieval Reduces Perplexity of Retrieval-Augmented Language Models
Ehsan Doostmohammadi, Tobias Norlund, Marco Kuhlmann +1
Augmenting language models with a retrieval mechanism has been shown to significantly improve their performance while keeping the number of parameters low. Retrieval-augmented mode…
On the Generalization Ability of Retrieval-Enhanced Transformers
Tobias Norlund, Ehsan Doostmohammadi, Richard Johansson +1
Recent work on the Retrieval-Enhanced Transformer (RETRO) model has shown that off-loading memory from trainable weights to a retrieval database can significantly improve language…
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian
Nasrin Taghizadeh, Ehsan Doostmohammadi, Elham Seifossadat +2
We have released Sina-BERT, a language model pre-trained on BERT (Devlin et al., 2018) to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT…
Joint Persian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using BERT
Ehsan Doostmohammadi, Minoo Nassajian, Adel Rahimi
Words are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and…
Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging
Ehsan Doostmohammadi, Minoo Nassajian, Adel Rahimi
Ezafe is a grammatical particle in some Iranian languages that links two words together. Regardless of the important information it conveys, it is almost always not indicated in Pe…
Persian Keyphrase Generation Using Sequence-to-Sequence Models
Ehsan Doostmohammadi, Mohammad Hadi Bokaei, Hossein Sameti
Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text. Keyphrase extraction is a useful upstream task and can be used in various…
PerKey: A Persian News Corpus for Keyphrase Extraction and Generation
Ehsan Doostmohammadi, Mohammad Hadi Bokaei, Hossein Sameti
Keyphrases provide an extremely dense summary of a text. Such information can be used in many Natural Language Processing tasks, such as information retrieval and text summarizatio…
Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts
Ehsan Doostmohammadi, Minoo Nassajian
Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resources and the problem of tokenization. The Cuneiform Language Identificat…
Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
Ehsan Doostmohammadi, Hossein Sameti, Ali Saffar
This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and cat…