1 code implementation • EMNLP 2021 • Zheng Yuan, Shiva Taslimipoor, Christopher Davis, Christopher Bryant
In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English.
no code implementations • LREC (MWE) 2022 • Shiva Taslimipoor, Christopher Bryant, Zheng Yuan
Grammatical error correction (GEC) is the task of automatically correcting errors in text.
1 code implementation • LREC 2022 • Mariano Felice, Shiva Taslimipoor, Øistein E. Andersen, Paula Buttery
Open cloze tests are a standard type of exercise where examinees must complete a text by filling in the gaps without any given options to choose from.
no code implementations • 15 Jan 2024 • Christopher Davis, Andrew Caines, Øistein Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical.
no code implementations • 17 Jul 2023 • Andrew Caines, Luca Benedetto, Shiva Taslimipoor, Christopher Davis, Yuan Gao, Oeistein Andersen, Zheng Yuan, Mark Elliott, Russell Moore, Christopher Bryant, Marek Rei, Helen Yannakoudakis, Andrew Mullooly, Diane Nicholls, Paula Buttery
The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention.
no code implementations • Findings (ACL) 2022 • Mariano Felice, Shiva Taslimipoor, Paula Buttery
This paper presents the first multi-objective transformer model for constructing open cloze tests that exploits generation and discrimination capabilities to improve performance.
1 code implementation • COLING (MWE) 2020 • Shiva Taslimipoor, Sara Bahaadini, Ekaterina Kochmar
This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task.
no code implementations • ACL 2020 • Omid Rohanian, Marek Rei, Shiva Taslimipoor, Le An Ha
Metaphor is a linguistic device in which a concept is expressed by mentioning another.
no code implementations • LREC 2020 • Sian Gooding, Shiva Taslimipoor, Ekaterina Kochmar
Multiword expressions (MWEs) were shown to be useful in a number of NLP tasks.
no code implementations • LREC 2020 • David Strohmaier, Sian Gooding, Shiva Taslimipoor, Ekaterina Kochmar
The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses.
no code implementations • WS 2019 • Shiva Taslimipoor, Omid Rohanian, Le An Ha
Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems.
no code implementations • SEMEVAL 2019 • Shiva Taslimipoor, Omid Rohanian, Sara Mo{\v{z}}e
This paper describes the system submitted to the SemEval 2019 shared task 1 {`}Cross-lingual Semantic Parsing with UCCA{'}.
2 code implementations • NAACL 2019 • Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki, Le An Ha, Ruslan Mitkov
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
1 code implementation • 9 Sep 2018 • Shiva Taslimipoor, Omid Rohanian
This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification.
no code implementations • SEMEVAL 2018 • Shiva Taslimipoor, Omid Rohanian, Le An Ha, Gloria Corpas Pastor, Ruslan Mitkov
This paper describes the system submitted to SemEval 2018 shared task 10 {`}Capturing Dicriminative Attributes{'}.
no code implementations • SEMEVAL 2018 • Omid Rohanian, Shiva Taslimipoor, Richard Evans, Ruslan Mitkov
This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B.
no code implementations • RANLP 2017 • Omid Rohanian, Shiva Taslimipoor, Victoria Yaneva, Le An Ha
In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena.
no code implementations • WS 2017 • Shiva Taslimipoor, Omid Rohanian, Ruslan Mitkov, Afsaneh Fazly
This study investigates the supervised token-based identification of Multiword Expressions (MWEs).
no code implementations • LREC 2012 • Shiva Taslimipoor, Afsaneh Fazly, Ali Hamzeh
In particular, LVCs are formed semi-productively: often a semantically-general verb (such as take) combines with a number of semantically-similar nouns to form semantically-related LVCs, as in make a decision/choice/commitment.