3,712 research outputs found
The Utility of Text: The Case of Amicus Briefs and the Supreme Court
We explore the idea that authoring a piece of text is an act of maximizing
one's expected utility. To make this idea concrete, we consider the societally
important decisions of the Supreme Court of the United States. Extensive past
work in quantitative political science provides a framework for empirically
modeling the decisions of justices and how they relate to text. We incorporate
into such a model texts authored by amici curiae ("friends of the court"
separate from the litigants) who seek to weigh in on the decision, then
explicitly model their goals in a random utility model. We demonstrate the
benefits of this approach in improved vote prediction and the ability to
perform counterfactual analysis.Comment: Working draf
Coherence in Machine Translation
Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and
with no access to extra-sentential context.
In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as
evidenced via discourse relations.
For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax
model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined
lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text
Past, Present And Future Implications Of Human Supervisory Control In Space Missions
Achieving the United Statesā Vision for future Space Exploration will necessitate far greater collaboration between humans and automated technology than previous space initiatives. However, the development of methodologies to optimize this collaboration currently lags behind development of the technologies themselves, thus potentially decreasing mission safety, efficiency and probability of success. This paper discusses the human supervisory control (HSC) implications for use in space, and outlines several areas of current automated space technology in which the function allocation between humans and machines/automation is sub-optimal or under dispute, including automated spacecraft landings, Mission Control, and wearable extra-vehicular activity computers. Based on these case studies, we show that a more robust HSC research program will be crucial to achieving the Vision for Space Exploration, especially given the limited resources under which it must be accomplished
Examining lexical coherence in a multilingual setting
This paper presents a preliminary study of lexical coherence and cohesion in the context of multiple languages.
We explore two entity-based frameworks in a multilingual setting in an attempt to understand how lexical coherence is realised across different languages. These frameworks (an entity-grid model and an entity graph metric) have previously been used for assessing coherence in a monolingual setting. We apply them to a multilingual setting for the first time, assessing whether entity based coherence frameworks could help ensure lexical coherence in a Machine Translation context
Developing Lunar Landing Vehicle Display Requirements through Content Analysis of Apollo Lunar Landing Voice Communications
The lengthy period since the Apollo landings limits present-day engineers attempting to draw from the experiences of veteran Apollo engineers and astronauts in the design of a new lunar lander. In order to circumvent these limitations, content analyses were performed on the voice transcripts of the Apollo lunar landing missions. The analyses highlighted numerous
inefficiencies in the design of the Apollo Lunar Module displays, particularly in the substantial use of the cognitive resources of the Lunar Module Pilot in the performance of low-level tasks. The results were used to generate functional and information requirements for the next-generation lunar lander cockpit.This research was sponsored by NASA and Draper Laboratory
Regional sentiment bias in social media reporting during crises
Crisis events such as terrorist attacks are extensively commented upon on social media platforms such as Twitter. For this reason, social media content posted during emergency events is increasingly being used by news media and in social studies to characterize the publicās reaction to those events. This is typically achieved by having journalists select ārepresentativeā tweets to show, or a classifier trained on prior human-annotated tweets is used to provide a sentiment/emotion breakdown for the event. However, social media users, journalists and annotators do not exist in isolation, they each have their own context and world view. In this paper, we ask the question, āto what extent do local and international biases affect the sentiments expressed on social media and the way that social media content is interpreted by annotatorsā. In particular, we perform a multi-lingual study spanning two events and three languages. We show that there are marked disparities between the emotions expressed by users in different languages for an event. For instance, during the 2016 Paris attack, there was 16% more negative comments written in the English than written in French, even though the event originated on French soil. Furthermore, we observed that sentiment biases also affect annotators from those regions, which can negatively impact the accuracy of social media labelling efforts. This highlights the need to consider the sentiment biases of users in different countries, both when analysing events through the lens of social media, but also when using social media as a data source, and for training automatic classification models
Commercial Bath Sponge (Spongia and Hippospongia) and total sponge community abundance and biomass estimates in the Florida middle and upper Keys, USA
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