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Found 35 papers, 9 papers with code
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models
Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms.
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels
This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level.
Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).
Probing for idiomaticity in vector space models
Contextualised word representation models have been successfully used for capturing different word usages and they may be an attractive alternative for representing idiomaticity in language.
Multistage BiCross encoder for multilingual access to COVID-19 health information
The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online.
Measuring What Counts: The case of Rumour Stance Classification
This paper specifically questions the evaluation metrics used in these shared tasks.
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media.
Ranked #1 on
Hate Speech Detection
on ToLD-Br
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
Measuring the Impact of Readability Features in Fake News Detection
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health.
Data-Driven Sentence Simplification: Survey and Benchmark
Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand.
Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality
To that end, we report experiments on a dataset with newly-collected post-editing indicators and show their usefulness when estimating post-editing effort.
EASSE: Easier Automatic Sentence Simplification Evaluation
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems.
Cross-Sentence Transformations in Text Simplification
Current approaches to Text Simplification focus on simplifying sentences individually.
Sheffield Submissions for the WMT18 Quality Estimation Shared Task
In this paper we present the University of Sheffield submissions for the WMT18 Quality Estimation shared task.
Sheffield Submissions for WMT18 Multimodal Translation Shared Task
For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting
A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language.
Learning Simplifications for Specific Target Audiences
Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text.
Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data.
Ranked #7 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
MUSST: A Multilingual Syntactic Simplification Tool
Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications).
Improving Evaluation of Document-level Machine Translation Quality Estimation
Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable.
Quality Estimation for Language Output Applications
The goal of QE is to estimate the quality of language output applications without the need of human references.
A Reading Comprehension Corpus for Machine Translation Evaluation
Effectively assessing Natural Language Processing output tasks is a challenge for research in the area.
