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Found 50 papers, 15 papers with code
Revisiting Rumour Stance Classification: Dealing with Imbalanced Data
Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours.
Controlling Extra-Textual Attributes about Dialogue Participants: A Case Study of English-to-Polish Neural Machine Translation
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification
Second, we conduct the first meta-evaluation of automatic metrics in Text Simplification, using our new data set (and other existing data) to analyze the variation of the correlation between metrics’ scores and human judgments across three dimensions: the perceived simplicity level, the system type, and the set of references used for computation.
Personalised Language Modelling of Screen Characters Using Rich Metadata Annotations
Personalisation of language models for dialogue sensitises them to better capture the speaking patterns of people of specific characteristics, and/or in specific environments.
Evaluating the Role of Target Arguments in Rumour Stance Classification
Considering a conversation thread, stance classification aims to identify the opinion (e. g. agree or disagree) of replies towards a given target.
Team SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining.
VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter
Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.
Ranked #1 on
Lay Summarization
on PLOS
Classifying COVID-19 vaccine narratives
One of the reasons behind this is vaccine disinformation which widely spreads in social media.
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity
This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity.
Sample Efficient Approaches for Idiomaticity Detection
In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection.
Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign.
Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.
Improving Tokenisation by Alternative Treatment of Spaces
We find that our modified algorithms lead to improved performance on downstream NLP tasks that involve handling complex words, whilst having no detrimental effect on performance in general natural language understanding tasks.
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 #8 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.
