408,559 research outputs found
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
We propose a model to automatically describe changes introduced in the source
code of a program using natural language. Our method receives as input a set of
code commits, which contains both the modifications and message introduced by
an user. These two modalities are used to train an encoder-decoder
architecture. We evaluated our approach on twelve real world open source
projects from four different programming languages. Quantitative and
qualitative results showed that the proposed approach can generate feasible and
semantically sound descriptions not only in standard in-project settings, but
also in a cross-project setting.Comment: Accepted at ACL 201
The REVERE project:Experiments with the application of probabilistic NLP to systems engineering
Despite natural language’s well-documented shortcomings as a medium for precise technical description, its use in software-intensive systems engineering remains inescapable. This poses many problems for engineers who must derive problem understanding and synthesise precise solution descriptions from free text. This is true both for the largely unstructured textual descriptions from which system requirements are derived, and for more formal documents, such as standards, which impose requirements on system development processes. This paper describes experiments that we have carried out in the REVERE1 project to investigate the use of probabilistic natural language processing techniques to provide systems engineering support
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
A Guided Tour Of Conceptual Engineering and Conceptual Ethics
In this Introduction, we aim to introduce the reader to the basic topic of this book. As part of this, we explain why we are using two different expressions (‘conceptual engineering’ and ‘conceptual ethics’) to describe the topics in the book. We then turn to some of the central foundational issues that arise for conceptual engineering and conceptual ethics, and finally we outline various views one might have about their role in philosophy and inquiry more generally
Subjects, Models, Languages, Transformations
Discussions about model-driven approaches tend to be hampered by terminological confusion. This is at least partially caused by a lack of formal precision in defining the basic concepts, including that of "model" and "thing being modelled" - which we call subject in this paper. We propose a minimal criterion that a model should fulfill: essentially, it should come equipped with a clear and unambiguous membership test; in other words, a notion of which subjects it models. We then go on to discuss a certain class of models of models that we call languages, which apart from defining their own membership test also determine membership of their members. Finally, we introduce transformations on each of these layers: a subject transformation is essentially a pair of subjects, a model transformation is both a pair of models and a model of pairs (namely, subject transformations), and a language transformation is both a pair of languages and a language of model transformations. We argue that our framework has the benefits of formal precision (there can be no doubt about whether something satifies our criteria for being a model, a language or a transformation) and minimality (it is hard to imagine a case of modelling or transformation not having the characterstics that we propose)
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypassing any intermediate meaning representations. These models
are immediately deployed online to solicit feedback from real users to flag
incorrect queries. Finally, the popularity of SQL facilitates gathering
annotations for incorrect predictions using the crowd, which is directly used
to improve our models. This complete feedback loop, without intermediate
representations or database specific engineering, opens up new ways of building
high quality semantic parsers. Experiments suggest that this approach can be
deployed quickly for any new target domain, as we show by learning a semantic
parser for an online academic database from scratch.Comment: Accepted at ACL 201
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