Finding Convincing Arguments Using Scalable Bayesian Preference Learning

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.Comment: Accepted for publication in TACL. To be presented at ACL 201

A Bayesian Approach for Sequence Tagging with Crowds

Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data. However, annotators are often unreliable and current aggregation methods cannot capture common types of span annotation errors. To address this, we propose a Bayesian method for aggregating sequence tags that reduces errors by modelling sequential dependencies between the annotations as well as the ground-truth labels. By taking a Bayesian approach, we account for uncertainty in the model due to both annotator errors and the lack of data for modelling annotators who complete few tasks. We evaluate our model on crowdsourced data for named entity recognition, information extraction and argument mining, showing that our sequential model outperforms the previous state of the art. We also find that our approach can reduce crowdsourcing costs through more effective active learning, as it better captures uncertainty in the sequence labels when there are few annotations.Comment: Accepted for EMNLP 201

Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive summaries are more faithful to their sources, they may be less readable and contain redundant or unnecessary information. This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries. We define a compound reward function that learns from keywords of interest and pairwise preference labels, which we use to fine-tune a pretrained abstractive summariser via offline reinforcement learning. We carry out both automated and human evaluation on three datasets, finding that our method outperforms a comparable extractive TLS method on two of the three benchmark datasets, and participants prefer our method's summaries to those of both the extractive TLS method and the pretrained abstractive model. The method does not require expensive reference summaries and needs only a small number of preferences to align the generated summaries with human preferences.Comment: ECAI 202

How young people find out about their family history of Huntington's disease

Family communication about adult-onset hereditary illness can be problematic, leaving some relatives inadequately informed or ignorant of their risk. Although studies have explored the barriers and facilitators in family communication about genetic risk, questions remain about when, what, how and indeed whether to tell relatives. The process of disclosure is also dependent upon the way in which genetic information is realized and understood by recipients, but research here is limited. Our paper explores young people’s experiences of finding out about a family history of the hereditary disorder Huntington’s disease (HD). In-depth interviews explored how and when young people found out, their reactions to different communication styles and any impact on family relations. We recruited young people through the North of Scotland regional genetics clinic and the Scottish Huntington’s Association (SHA). Thirtythree young people (aged 9–28) were interviewed. A qualitative analysis was undertaken which revealed four types of disclosure experiences: (1) having always been told, (2) gradually told, (3) HD was kept a secret, or (4) HD as a new diagnosis. In particular, the timing and style of disclosure from relatives, and one’s stage of awareness, were fundamental in structuring participants’ accounts. This article focuses on questions of when, how and indeed whether to tell children, and sits within a broader set of research and practice issues about what professionals and families (should) tell children about parental illness and genetic risk.Wellcome Trust’s Programme in Biomedical Ethic

Personal Consultation and Contractual Planning in Stimulating Faculty Growth: The Faculty Development Program at Northern Illinois University

The Development of Faculty Development at NIU Barriers to Development Conducting the Program Individual Cases Faculty Reactions to the Program Future Development of the Program Conclusio