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Found 45 papers, 19 papers with code
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection
This mismatch can be partially attributed to the limitations of current evaluation setups that neglect the rapid evolution of online content and the underlying social graph.
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.
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers
Long-sequence transformers are designed to improve the representation of longer texts by language models and their performance on downstream document-level tasks.
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing attention within the research comminity.
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs
We present FewShotTextGCN, a novel method designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings.
Scientific and Creative Analogies in Pretrained Language Models
This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2.
Learning New Tasks from a Few Examples with Soft-Label Prototypes
Existing approaches to few-shot learning in NLP rely on large language models and fine-tuning of these to generalise on out-of-distribution data.
Ruddit: Norms of Offensiveness for English Reddit Comments
On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds.
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks.
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability
Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language.
Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers
We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision.
Multilingual and cross-lingual document classification: A meta-learning approach
The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods.
Cross-Lingual Document Classification
Document Classification
+2
A Multimodal Framework for the Detection of Hateful Memes
An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes.
The Teacher-Student Chatroom Corpus
The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English.
Analyzing Neural Discourse Coherence Models
In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation.
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications.
Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions.
Graph-based Modeling of Online Communities for Fake News Detection
Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as the demographic traits of users who interact with them.
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions
We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Joint Modelling of Emotion and Abusive Language Detection
The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online.
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods
Abuse on the Internet represents an important societal problem of our time.
Neural and FST-based approaches to grammatical error correction
In this paper, we describe our submission to the BEA 2019 shared task on grammatical error correction.
Multi-Task Learning for Coherence Modeling
We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment.
Context is Key: Grammatical Error Detection with Contextual Word Representations
Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners.
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors
While rule-based detection of subject-verb agreement (SVA) errors is sensitive to syntactic parsing errors and irregularities and exceptions to the main rules, neural sequential labelers have a tendency to overfit their training data.
CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweets
We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data.
Abusive Language Detection with Graph Convolutional Networks
Abuse on the Internet represents a significant societal problem of our time.
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification
Most current approaches to metaphor identification use restricted linguistic contexts, e. g. by considering only a verb's arguments or the sentence containing a phrase.
Author Profiling for Hate Speech Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet.
Neural Character-based Composition Models for Abuse Detection
The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words.
Author Profiling for Abuse Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet.
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input
We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences.
Neural Sequence-Labelling Models for Grammatical Error Correction
We propose an approach to N-best list reranking using neural sequence-labelling models.
Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions
Frame-semantic parsing and semantic role labelling, that aim to automatically assign semantic roles to arguments of verbs in a sentence, have become an active strand of research in NLP.
Auxiliary Objectives for Neural Error Detection Models
We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing.
Ranked #2 on
Grammatical Error Detection
on CoNLL-2014 A1
Compositional Sequence Labeling Models for Error Detection in Learner Writing
In this paper, we present the first experiments using neural network models for the task of error detection in learner writing.
Ranked #3 on
Grammatical Error Detection
on CoNLL-2014 A1
Automatic Text Scoring Using Neural Networks
Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking.
