Improved Coreference Resolution Using Cognitive Insights

Coreference resolution is the task of extracting referential expressions, or mentions, in text and clustering these by the entity or concept they refer to. The sustained research interest in the task reflects the richness of reference expression usage in natural language and the difficulty in encoding insights from linguistic and cognitive theories effectively. In this thesis, we design and implement LIMERIC, a state-of-the-art coreference resolution engine. LIMERIC naturally incorporates both non-local decoding and entity-level modelling to achieve the highly competitive benchmark performance of 64.22% and 59.99% on the CoNLL-2012 benchmark with a simple model and a baseline feature set. As well as strong performance, a key contribution of this work is a reconceptualisation of the coreference task. We draw an analogy between shift-reduce parsing and coreference resolution to develop an algorithm which naturally mimics cognitive models of human discourse processing. In our feature development work, we leverage insights from cognitive theories to improve our modelling. Each contribution achieves statistically significant improvements and sum to gains of 1.65% and 1.66% on the CoNLL-2012 benchmark, yielding performance values of 65.76% and 61.27%. For each novel feature we propose, we contribute an accompanying analysis so as to better understand how cognitive theories apply to real language data. LIMERIC is at once a platform for exploring cognitive insights into coreference and a viable alternative to current systems. We are excited by the promise of incorporating our and further cognitive insights into more complex frameworks since this has the potential to both improve the performance of computational models, as well as our understanding of the mechanisms underpinning human reference resolution

Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias

The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are "hallucinatory", e.g., disambiguating gender-ambiguous occurrences of 'doctor' as male doctors. We show that for languages with type B reflexivization, e.g., Swedish and Russian, we can construct multi-task challenge datasets for detecting gender bias that lead to unambiguously wrong model predictions: In these languages, the direct translation of 'the doctor removed his mask' is not ambiguous between a coreferential reading and a disjoint reading. Instead, the coreferential reading requires a non-gendered pronoun, and the gendered, possessive pronouns are anti-reflexive. We present a multilingual, multi-task challenge dataset, which spans four languages and four NLP tasks and focuses only on this phenomenon. We find evidence for gender bias across all task-language combinations and correlate model bias with national labor market statistics.Comment: To appear in EMNLP 202

Salt reduction in Australia: from advocacy to action

BACKGROUND: As part of its endorsement of the World Health Organization\u27s Global Action Plan to prevent non-communicable diseases, the Federal Government of Australia has committed to a 30% reduction in average population salt intake by 2025. Currently, mean daily salt intake levels are 8-9 g, varying by sex, region and population group. A number of salt reduction initiatives have been established over the last decade, but key elements for a co-ordinated population-level strategy are still missing. The objective of this review is to provide a comprehensive overview of existing population-level salt reduction activities in Australia and identify opportunities for further action. METHODS: A review of the published literature and stakeholder activities was undertaken to identify and document current activities. The activities were then assessed against a pre-defined framework for salt reduction strategies. RESULTS: A range of initiatives were identified from the review. The Australian Division of World Action on Salt and Health (AWASH) was established in 2005 and in 2007 launched its Drop the Salt! Campaign. This united non-governmental organisations (NGOs), health and medical and food industry organisations in a co-ordinated advocacy effort to encourage government to develop a national strategy to reduce salt. Subsequently, in 2010 the Federal Government launched its Food and Health Dialogue (FHD) with a remit to improve the health of the food supply in Australia through voluntary partnerships with food industry, government and non-government public health organisations. The focus of the FHD to date has been on voluntary reformulation of foods, primarily through salt reduction targets. More recently, in December 2014, the government\u27s Health Star Rating system was launched. This front of pack labelling scheme uses stars to highlight the nutritional profile of packaged foods. Both government initiatives have clear targets or criteria for industry to meet, however, both are voluntary and the extent of industry uptake is not yet clear. There is also no parallel public awareness campaign to try and influence consumer behaviour relating to salt and no agreed mechanism for monitoring national changes in salt intake. The Victorian Health Promotion Foundation (VicHealth) has recently instigated a State-level partnership to advance action and will launch its strategy in 2015. CONCLUSIONS: In conclusion, salt reduction activities are currently being implemented through a variety of different programs but additional efforts and more robust national monitoring mechanisms are required to ensure that Australia is on track to achieve the proposed 30% reduction in salt intake within the next decade

How to write a bias statement:Recommendations for submissions to the Workshop on Gender Bias in NLP

At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications. For the 2020 edition of the workshop, we therefore requested that all authors include an explicit bias statement in their work to clarify how their work relates to the social context in which NLP systems are used. The programme committee of the workshops included a number of reviewers with a background in the humanities and social sciences, in addition to NLP experts doing the bulk of the reviewing. Each paper was assigned one of those reviewers, and they were asked to pay specific attention to the provided bias statements in their reviews. This initiative was well received by the authors who submitted papers to the workshop, several of whom said they received useful suggestions and literature hints from the bias reviewers. We are therefore planning to keep this feature of the review process in future editions of the workshop.Comment: This document was originally published as a blog post on the web site of GeBNLP 202