201 citations · 1k across the 85 of their papers we have counts for
17 papers · 1 filter
Multi-Sense Language Modelling
Andrea Lekkas, Peter Schneider-Kamp, Isabelle Augenstein
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 meani…
Longitudinal Citation Prediction using Temporal Graph Neural Networks
Andreas Nugaard Holm, Barbara Plank, Dustin Wright +1
Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time. Prior work viewed this as a static prediction task. As papers…
SIGTYP 2020 Shared Task: Prediction of Typological Features
Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke +6
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be use…
What Can We Do to Improve Peer Review in NLP?
Anna Rogers, Isabelle Augenstein
Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers…
Unsupervised Evaluation for Question Answering with Transformers
Lukas Muttenthaler, Isabelle Augenstein, Johannes Bjerva
It is challenging to automatically evaluate the answer of a QA model at inference time. Although many models provide confidence scores, and simple heuristics can go a long way towa…
Data-Efficient Pretraining via Contrastive Self-Supervision
Nils Rethmeier, Isabelle Augenstein
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…
A Diagnostic Study of Explainability Techniques for Text Classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity. Efforts to make the rationales beh…
Generating Label Cohesive and Well-Formed Adversarial Claims
Pepa Atanasova, Dustin Wright, Isabelle Augenstein
Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, w…
Transformer Based Multi-Source Domain Adaptation
Dustin Wright, Isabelle Augenstein
In practical machine learning settings, the data on which a model must make predictions often come from a different distribution than the data it was trained on. Here, we investiga…
Time-Aware Evidence Ranking for Fact-Checking
Liesbeth Allein, Isabelle Augenstein, Marie-Francine Moens
Truth can vary over time. Fact-checking decisions on claim veracity should therefore take into account temporal information of both the claim and supporting or refuting evidence. I…
Multi-Hop Fact Checking of Political Claims
Wojciech Ostrowski, Arnav Arora, Pepa Atanasova +1
Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set…
Inducing Language-Agnostic Multilingual Representations
Wei Zhao, Steffen Eger, Johannes Bjerva +1
Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world. However, they currently require large pretraining…
2kenize: Tying Subword Sequences for Chinese Script Conversion
Pranav A, Isabelle Augenstein
Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do…
Generating Fact Checking Explanations
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
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 recen…
SubjQA: A Dataset for Subjectivity and Review Comprehension
Johannes Bjerva, Nikita Bhutani, Behzad Golshan +2
Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-…
Zero-Shot Cross-Lingual Transfer with Meta Learning
Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva +1
Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is…
Claim Check-Worthiness Detection as Positive Unlabelled Learning
Dustin Wright, Isabelle Augenstein
As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study th…