Search Results for author:
Found 115 papers, 49 papers with code
USFD: Twitter NER with Drift Compensation and Linked Data
This paper describes a pilot NER system for Twitter, comprising the USFD system entry to the W-NUT 2015 NER shared task.
Monolingual Social Media Datasets for Detecting Contradiction and Entailment
Entailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined.
Stance Detection with Bidirectional Conditional Encoding
Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral".
Numerically Grounded Language Models for Semantic Error Correction
Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction.
emoji2vec: Learning Emoji Representations from their Description
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings.
Generalisation in Named Entity Recognition: A Quantitative Analysis
Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire.
Multi-Task Learning of Keyphrase Boundary Classification
Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types.
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A).
Ranked #1 on
Stance Detection
on RumourEval
Latent Multi-task Architecture Learning
In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses.
A Supervised Approach to Extractive Summarisation of Scientific Papers
Automatic summarisation is a popular approach to reduce a document to its main arguments.
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Identifying public misinformation is a complicated and challenging task.
Ranked #5 on
Fake News Detection
on FNC-1
Tracking Typological Traits of Uralic Languages in Distributed Language Representations
Although linguistic typology has a long history, computational approaches have only recently gained popularity.
Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers
We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers.
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings
A core part of linguistic typology is the classification of languages according to linguistic properties, such as those detailed in the World Atlas of Language Structure (WALS).
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets.
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets
We take a multi-task learning approach to the shared Task 1 at SemEval-2018.
Jack the Reader - A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
Character-level Supervision for Low-resource POS Tagging
Neural part-of-speech (POS) taggers are known to not perform well with little training data.
Jack the Reader -- A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
A strong baseline for question relevancy ranking
The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks -- a task that amounts to question relevancy ranking -- involve complex pipelines and manual feature engineering.
Parameter sharing between dependency parsers for related languages
We find that sharing transition classifier parameters always helps, whereas the usefulness of sharing word and/or character LSTM parameters varies.
Nightmare at test time: How punctuation prevents parsers from generalizing
Punctuation is a strong indicator of syntactic structure, and parsers trained on text with punctuation often rely heavily on this signal.
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding
This paper documents the Team Copenhagen system which placed first in the CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection, Task 2 with an overall accuracy of 49. 87.
What do Language Representations Really Represent?
If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations.
A Probabilistic Generative Model of Linguistic Typology
In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features.
Combining Sentiment Lexica with a Multi-View Variational Autoencoder
When assigning quantitative labels to a dataset, different methodologies may rely on different scales.
Issue Framing in Online Discussion Fora
In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic.
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling
Studying the ways in which language is gendered has long been an area of interest in sociolinguistics.
Uncovering Probabilistic Implications in Typological Knowledge Bases
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions.
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
Although the vast majority of knowledge bases KBs are heavily biased towards English, Wikipedias do cover very different topics in different languages.
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models
Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data.
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification.
Back to the Future -- Sequential Alignment of Text Representations
In particular, language evolution causes data drift between time-steps in sequential decision-making tasks.
Domain Transfer in Dialogue Systems without Turn-Level Supervision
Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation.
Retrieval-based Goal-Oriented Dialogue Generation
Most research on dialogue has focused either on dialogue generation for openended chit chat or on state tracking for goal-directed dialogue.
Mapping (Dis-)Information Flow about the MH17 Plane Crash
In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash.
Joint Emotion Label Space Modelling for Affect Lexica
Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection.
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training.
Explainable Artificial Intelligence (XAI)
Model Compression
+1
Claim Check-Worthiness Detection as Positive Unlabelled Learning
In applying this, we out-perform the state of the art in two of the three tasks studied for claim check-worthiness detection in English.
Zero-Shot Cross-Lingual Transfer with Meta Learning
We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.
Generating Fact Checking Explanations
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims.
SubjQA: A Dataset for Subjectivity and Review Comprehension
We find that subjectivity is also an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance.
2kenize: Tying Subword Sequences for Chinese Script Conversion
Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP.
Multi-Hop Fact Checking of Political Claims
We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop.
Transformer Based Multi-Source Domain Adaptation
Here, we investigate the problem of unsupervised multi-source domain adaptation, where a model is trained on labelled data from multiple source domains and must make predictions on a domain for which no labelled data has been seen.
Generating Label Cohesive and Well-Formed Adversarial Claims
However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in.
A Diagnostic Study of Explainability Techniques for Text Classification
Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity.
Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data
We thus approach pretraining from a miniaturisation perspective, such as not to require massive external data sources and models, or learned translations from continuous input embeddings to discrete labels.
Data-Efficient Pretraining via Contrastive Self-Supervision
For natural language processing `text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data.
Unsupervised Evaluation for Question Answering with Transformers
We observe a consistent pattern in the answer representations, which we show can be used to automatically evaluate whether or not a predicted answer span is correct.
What Can We Do to Improve Peer Review in NLP?
Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious.
SIGTYP 2020 Shared Task: Prediction of Typological Features
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages.
Multi-Sense Language Modelling
The effectiveness of a language model is influenced by its token representations, which must encode contextual information and handle the same word form having a plurality of meanings (polysemy).
Longitudinal Citation Prediction using Temporal Graph Neural Networks
Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time.
Disembodied Machine Learning: On the Illusion of Objectivity in NLP
In this paper, we contextualise this discourse of bias in the ML community against the subjective choices in the development process.
Does Typological Blinding Impede Cross-Lingual Sharing?
Our hypothesis is that a model trained in a cross-lingual setting will pick up on typological cues from the input data, thus overshadowing the utility of explicitly using such features.
White Paper - Creating a Repository of Objectionable Online Content: Addressing Undesirable Biases and Ethical Considerations
This white paper summarizes the authors' structured brainstorming regarding ethical considerations for creating an extensive repository of online content labeled with tags that describe potentially questionable content for young viewers.
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives
Contrastive self-supervised training objectives enabled recent successes in image representation pretraining by learning to contrast input-input pairs of augmented images as either similar or dissimilar.
A Survey on Stance Detection for Mis- and Disinformation Identification
Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information).
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.
University of Copenhagen Participation in TREC Health Misinformation Track 2020
Our approach consists of 3 steps: (1) we create an initial run with BM25 and RM3; (2) we estimate credibility and misinformation scores for the documents in the initial run; (3) we merge the relevance, credibility and misinformation scores to re-rank documents in the initial run.
A Neighbourhood Framework for Resource-Lean Content Flagging
The framework is based on a nearest-neighbour architecture.
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models
Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language.
Cross-Domain Label-Adaptive Stance Detection
In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them.
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
Scientific document understanding is challenging as the data is highly domain specific and diverse.
Determining the Credibility of Science Communication
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct.
Is Sparse Attention more Interpretable?
Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs.
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress.
Towards Explainable Fact Checking
This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine the veracity of claims given evidence documents.
Semi-Supervised Exaggeration Detection of Health Science Press Releases
Given this, we present a formalization of and study into the problem of exaggeration detection in science communication.
Diagnostics-Guided Explanation Generation
When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model.
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection.
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations.
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
In addition, we show the applicability of our approach in a completely unsupervised setting.
Quantifying Gender Biases Towards Politicians on Reddit
Rather than overt hostile or benevolent sexism, the results of the nominal and lexical analyses suggest this interest is not as professional or respectful as that expressed about male politicians.
A Survey on Gender Bias in Natural Language Processing
3) Despite a myriad of papers on gender bias in NLP methods, we find that most of the newly developed algorithms do not test their models for bias and disregard possible ethical considerations of their work.
A Latent-Variable Model for Intrinsic Probing
The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information.
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.
Ranked #1 on
Document Classification
on SciDocs (MeSH)
Generating Scientific Claims for Zero-Shot Scientific Fact Checking
To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims.
Probing Pre-Trained Language Models for Cross-Cultural Differences in Values
In this paper, we introduce probes to study which values across cultures are embedded in these models, and whether they align with existing theories and cross-cultural value surveys.
Fact Checking with Insufficient Evidence
To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions.
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models
The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision.
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection
Especially, construct-driven CAD -- perturbations of core features -- may induce models to ignore the context in which core features are used.
Machine Reading, Fast and Slow: When Do Models "Understand" Language?
Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmarks for the 'right' reasons; and (b) to understand what those reasons would even be.
Modeling Information Change in Science Communication with Semantically Matched Paraphrases
Whether the media faithfully communicate scientific information has long been a core issue to the science community.
Revisiting Softmax for Uncertainty Approximation in Text Classification
A cheaper alternative is to simply use the softmax based on a single forward pass without dropout to estimate model uncertainty.
Multi-View Knowledge Distillation from Crowd Annotations for Out-of-Domain Generalization
Selecting an effective training signal for tasks in natural language processing is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable.
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
For that study, we introduce TempEL, an entity linking dataset that consists of time-stratified English Wikipedia snapshots from 2013 to 2022, from which we collect both anchor mentions of entities, and these target entities' descriptions.
Measuring Gender Bias in West Slavic Language Models
Perhaps surprisingly, Czech, Slovak, and Polish language models produce more hurtful completions with men as subjects, which, upon inspection, we find is due to completions being related to violence, death, and sickness.
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing
Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP).
Multilingual Event Extraction from Historical Newspaper Adverts
We find that: 1) even with scarce annotated data, it is possible to achieve surprisingly good results by formulating the problem as an extractive QA task and leveraging existing datasets and models for modern languages; and 2) cross-lingual low-resource learning for historical languages is highly challenging, and machine translation of the historical datasets to the considered target languages is, in practice, often the best-performing solution.
Measuring Intersectional Biases in Historical Documents
We find that there is a trade-off between the stability of the word embeddings and their compatibility with the historical dataset.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Faithfulness Tests for Natural Language Explanations
Explanations of neural models aim to reveal a model's decision-making process for its predictions.
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
The results show that our method outperforms the state-of-the-art with an average of $3. 5$ F1 points increase in-domain, and is more generalizable with an averaged increase of $10. 2$ F1 on out-of-domain evaluation while using $\leq10\%$ of the training data.
Ranked #1 on
Stance Detection
on mtsd
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
The moderation of content on online platforms is usually non-transparent.
Explaining Interactions Between Text Spans
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI).
PHD: Pixel-Based Language Modeling of Historical Documents
We then pre-train our model, PHD, on a combination of synthetic scans and real historical newspapers from the 1700-1900 period.
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection
CADs introduce minimal changes to existing training data points and flip their labels; training on them may reduce model dependency on spurious features.
Social Bias Probing: Fairness Benchmarking for Language Models
While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
Grammatical Gender's Influence on Distributional Semantics: A Causal Perspective
However, when we control for the meaning of the noun, we find that grammatical gender has a near-zero effect on adjective choice, thereby calling the neo-Whorfian hypothesis into question.
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
We systematically study the effects of the phenomenon across NLI models for $\textbf{in-}$ and $\textbf{out-of-}$ domain settings.
Understanding Fine-grained Distortions in Reports of Scientific Findings
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions.
Investigating the Impact of Model Instability on Explanations and Uncertainty
In this large-scale empirical study, we insert different levels of noise perturbations and measure the effect on the output of pre-trained language models and different uncertainty metrics.
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?
We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations.
Machine Reading, Fast and Slow: When Do Models “Understand” Language?
Two of the most fundamental issues in Natural Language Understanding (NLU) at present are: (a) how it can established whether deep learning-based models score highly on NLU benchmarks for the ”right” reasons; and (b) what those reasons would even be.
Multi3Generation: Multitask, Multilingual, Multimodal Language Generation
This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation.
