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Found 20 papers, 5 papers with code
CEPOC: The Cambridge Exams Publishing Open Cloze dataset
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.
Improving Grammatical Error Correction for Multiword Expressions
Grammatical error correction (GEC) is the task of automatically correcting errors in text.
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
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.
Prompting open-source and commercial language models for grammatical error correction of English learner text
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical.
On the application of Large Language Models for language teaching and assessment technology
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.
Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers
This paper presents the first multi-objective transformer model for constructing open cloze tests that exploits generation and discrimination capabilities to improve performance.
MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models
This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task.
Verbal Multiword Expressions for Identification of Metaphor
Metaphor is a linguistic device in which a concept is expressed by mentioning another.
Incorporating Multiword Expressions in Phrase Complexity Estimation
Multiword expressions (MWEs) were shown to be useful in a number of NLP tasks.
SeCoDa: Sense Complexity Dataset
The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses.
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions
Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems.
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
This paper describes the system submitted to the SemEval 2019 shared task 1 {`}Cross-lingual Semantic Parsing with UCCA{'}.
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation
This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification.
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B.
Using Gaze Data to Predict Multiword Expressions
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.
Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context
This study investigates the supervised token-based identification of Multiword Expressions (MWEs).
Using Noun Similarity to Adapt an Acceptability Measure for Persian Light Verb Constructions
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.
