Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed. Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement. Care must be exercised by applications in executing the various algorithms that may be specified in an XML signature and in the processing of any “executable content” that might be provided to such algorithms as parameters, such as XSLT transforms. The algorithms specified in this document will usually be implemented via a trusted library but even there perverse parameters might cause unacceptable processing or memory demand.

For example, the transform could be a decompression routine given by a Java class appearing as a base64 encoded parameter to a Java Transform algorithm. However, applications should refrain from using application-specific transforms if they wish their signatures to be verifiable outside of their application domain. Transform Algorithms(section 6.6) defines the list of standard transformations. The structure of SignedInfo includes the canonicalization algorithm, a signature algorithm, and one or more references. The SignedInfo element may contain an optional ID attribute that will allow it to be referenced by other signatures and objects. Possible forms for identification include certificates, key names, and key agreement algorithms and information — we define only a few.

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope—i.e., queries that do not fall into any of the system’s supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class.

Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models. Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models. For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naively learning from related data fails to improve performance as the related data can be inconsistent with the target task. We describe a meta-learning based method that selectively learns from the related dialog task data.

Consequently, we use these capitalized key words to unambiguously specify requirements over protocol and application features and behavior that affect the interoperability and security of implementations. These key words are not used to describe XML grammar; schema definitions unambiguously describe such requirements and we wish to reserve the prominence of these terms for the natural language descriptions of protocols and features. For instance, an XML attribute might be described as being “optional.” Compliance with the Namespaces in XML specification [XML-NAMES] is described as “REQUIRED.” This resource on press freedom and the use of communication and media to articulate and defend human rights includes a number of strategic reflections on communication related to social change from Indian journalists and thinkers. She asserts that it is possible for community radio stations to challenge the hegemony of the mainstream media and its programming methods only by developing rigorous and appropriate codes of conduct and practice in the spirit of self-regulation.


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