1,031 research outputs found
Enhancing practical TAG parsing efficiency by capturing redundancy
International audienceParsing efficiency within the context of tree adjoining grammars (TAGs) depends not only on the size of the input sentence but also, linearly, on the size of the input TAG, which can attain several thousands of elementary trees. We propose a factorized, finite-state TAG representation which copes with this combinatorial explosion. The associated parsing algorithm substantially increases the parsing performance on a real-size French TAG grammar
Promoting multiword expressions in A* TAG parsing
International audienceMultiword expressions (MWEs) are pervasive in natural languages and often have both idiomatic and compositional readings, which leads to high syntactic ambiguity. We show that for some MWE types idiomatic readings are usually the correct ones. We propose a heuristic for an A* parser for Tree Adjoining Grammars which benefits from this knowledge by promoting MWE-oriented analyses. This strategy leads to a substantial reduction in the parsing search space in case of true positive MWE occurrences, while avoiding parsing failures in case of false positives
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Large Language Models for Software Engineering: A Systematic Literature Review
Large Language Models (LLMs) have significantly impacted numerous domains,
notably including Software Engineering (SE). Nevertheless, a well-rounded
understanding of the application, effects, and possible limitations of LLMs
within SE is still in its early stages. To bridge this gap, our systematic
literature review takes a deep dive into the intersection of LLMs and SE, with
a particular focus on understanding how LLMs can be exploited in SE to optimize
processes and outcomes. Through a comprehensive review approach, we collect and
analyze a total of 229 research papers from 2017 to 2023 to answer four key
research questions (RQs). In RQ1, we categorize and provide a comparative
analysis of different LLMs that have been employed in SE tasks, laying out
their distinctive features and uses. For RQ2, we detail the methods involved in
data collection, preprocessing, and application in this realm, shedding light
on the critical role of robust, well-curated datasets for successful LLM
implementation. RQ3 allows us to examine the specific SE tasks where LLMs have
shown remarkable success, illuminating their practical contributions to the
field. Finally, RQ4 investigates the strategies employed to optimize and
evaluate the performance of LLMs in SE, as well as the common techniques
related to prompt optimization. Armed with insights drawn from addressing the
aforementioned RQs, we sketch a picture of the current state-of-the-art,
pinpointing trends, identifying gaps in existing research, and flagging
promising areas for future study
Robustness in Coreference Resolution
Coreference resolution is the task of determining different expressions of a text that refer to the same entity. The resolution of coreferring expressions is an essential step for automatic interpretation of the text. While coreference information is beneficial for various NLP tasks like summarization, question answering, and information extraction, state-of-the-art coreference resolvers are barely used in any of these tasks. The problem is the lack of robustness in coreference resolution systems. A coreference resolver that gets higher scores on the standard
evaluation set does not necessarily perform better than the others on a new test set.
In this thesis, we introduce robustness in coreference resolution by (1) introducing a reliable evaluation framework for recognizing robust improvements, and (2) proposing a solution that results in robust coreference resolvers.
As the first step of setting up the evaluation framework, we introduce a reliable evaluation metric, called LEA, that overcomes the drawbacks of the existing metrics. We analyze LEA based on various types of errors in coreference outputs and show that it results in reliable scores. In addition to an evaluation metric, we also introduce an evaluation setting in which we disentangle coreference evaluations from parsing complexities. Coreference resolution is affected by parsing complexities for detecting the boundaries of expressions that have complex syntactic structures. We reduce the effect of parsing errors in coreference evaluation by automatically extracting a minimum span for each expression. We then emphasize the importance of out-of-domain evaluations and generalization in coreference resolution and discuss the reasons behind the poor generalization of state-of-the-art coreference resolvers.
Finally, we show that enhancing state-of-the-art coreference resolvers with linguistic features is a promising approach for making coreference resolvers robust across domains. The
incorporation of linguistic features with all their values does not improve the performance.
However, we introduce an efficient pattern mining approach, called EPM, that mines all feature-value combinations that are discriminative for coreference relations. We then only
incorporate feature-values that are discriminative for coreference relations. By employing EPM feature-values, performance improves significantly across various domains
A Model-Based System Engineering Approach to Support System Architecting Activities in Early Aircraft Design
The aviation industry aims to reduce its environmental footprint and meet ambitious environmental targets, prompting the exploration of novel aircraft concepts and systems, such as hybrid-electric or distributed propulsion. These emerging technologies introduce complexity to aircraft system architectures, requiring innovative approaches to design, optimization, and safety assessment, particularly for system architecting. Several aspects of system architecting specification and evaluation are typically performed separately, using different people and a mix of manual and model-based processes. Connecting these activities has the potential to make the design process more efficient and effective. This thesis explores how a Model-Based Systems Engineering (MBSE) specification environment can be structured and enriched to enable a better bridge to Multidisciplinary Design Analysis and Optimization (MDAO) and Model-Based Safety Assessment (MBSA) activities. The proposed MBSE approach focuses on enhancing system specifications, particularly for unconventional system architectures, which typically feature greater variability in early design stages. Using the ARCADIA/Capella MBSE environment, a multi-level approach is proposed to structure the system architecture specification and the Property Value Management Tool (PVMT) add-on is used to facilitate the bridge to other system architecting activities. In addition, a catalogue of modeling artifacts is established to facilitate the development of various hybrid-electric system configurations. The MDAO link mechanism is demonstrated with an example from the collaborative AGILE4.0 project. Two test cases demonstrate the implementation of the approach: a hybrid-electric propulsion system and associated sub-systems for the overall approach and the landing gear braking system for the model-based Functional Hazard Analysis (FHA), as an example of an MBSA activity. Overall, this thesis helps improve the integration and collaboration between engineers working on MBSE, MDAO, and MBSA. This better integration will help to reduce the development time and risk. Therefore, the presented thesis contributes to a more efficient aircraft development process, enabling the industry to tackle the emerging needs of unconventional aircraft systems and their integration
Large-scale Wireless Local-area Network Measurement and Privacy Analysis
The edge of the Internet is increasingly becoming wireless. Understanding the wireless edge is therefore important for understanding the performance and security aspects of the Internet experience. This need is especially necessary for enterprise-wide wireless local-area networks (WLANs) as organizations increasingly depend on WLANs for mission- critical tasks. To study a live production WLAN, especially a large-scale network, is a difficult undertaking. Two fundamental difficulties involved are (1) building a scalable network measurement infrastructure to collect traces from a large-scale production WLAN, and (2) preserving user privacy while sharing these collected traces to the network research community. In this dissertation, we present our experience in designing and implementing one of the largest distributed WLAN measurement systems in the United States, the Dartmouth Internet Security Testbed (DIST), with a particular focus on our solutions to the challenges of efficiency, scalability, and security. We also present an extensive evaluation of the DIST system. To understand the severity of some potential trace-sharing risks for an enterprise-wide large-scale wireless network, we conduct privacy analysis on one kind of wireless network traces, a user-association log, collected from a large-scale WLAN. We introduce a machine-learning based approach that can extract and quantify sensitive information from a user-association log, even though it is sanitized. Finally, we present a case study that evaluates the tradeoff between utility and privacy on WLAN trace sanitization
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