Search Results for author: Ayyoob Imani

Found 9 papers, 5 papers with code

RET-LLM: Towards a General Read-Write Memory for Large Language Models

1 code implementation23 May 2023 Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, Hinrich Schütze

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) through their extensive parameters and comprehensive data utilization.

Question Answering

Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging

1 code implementation18 Oct 2022 Ayyoob Imani, Silvia Severini, Masoud Jalili Sabet, François Yvon, Hinrich Schütze

An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages.

Part-Of-Speech Tagging POS +1

Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages

no code implementations LREC 2022 Silvia Severini, Ayyoob Imani, Philipp Dufter, Hinrich Schütze

Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages.

Bilingual Lexicon Induction Transliteration

Graph Algorithms for Multiparallel Word Alignment

1 code implementation EMNLP 2021 Ayyoob Imani, Masoud Jalili Sabet, Lütfi Kerem Şenel, Philipp Dufter, François Yvon, Hinrich Schütze

With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.

Link Prediction Machine Translation +3

ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

no code implementations ACL 2021 Ayyoob Imani, Masoud Jalili Sabet, Philipp Dufter, Michael Cysouw, Hinrich Schütze

With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective.

Multilingual NLP Transfer Learning

Deep Neural Networks for Query Expansion using Word Embeddings

no code implementations8 Nov 2018 Ayyoob Imani, Amir Vakili, Ali Montazer, Azadeh Shakery

In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods.

Information Retrieval Retrieval +1

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