Salesforce.com

About: Salesforce.com is a based out in . It is known for research contribution in the topics: User interface & Object (computer science). The organization has 2418 authors who have published 2775 publications receiving 63956 citations.

System, method and computer program product for distributed execution of related reports

Abstract: In accordance with embodiments, there are provided mechanisms and methods for distributed execution of related reports. These mechanisms and methods for distributed execution of related reports can enable embodiments to provide parallel execution of related reports. The ability of embodiments to provide parallel execution of related reports can result in higher performance in the execution of the related reports.

Learning when to skim and when to read.

Abstract: Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset. We demonstrate two approaches to reducing unnecessary computation in cases where a fast but weak baseline classier and a stronger, slower model are both available. Applying an AUC-based metric to the task of sentiment classification, we find significant efficiency gains with both a probability-threshold method for reducing computational cost and one that uses a secondary decision network.

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Abstract: Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.

Systems and methods for supporting transactional message handling

Abstract: In accordance with embodiments disclosed herein, there are provided methods, systems, and apparatuses including, for supporting transactional message handling in an on-demand service environment including, for example: enqueuing a message specifying a transaction to be processed via a host organization; inserting a row into a database of the host organization associating the message with a status of pending, wherein the row is autocommitted to the database upon insertion; updating the status for the row to ready if a commit operation for the transaction is initiated; requesting a lock on the row; and performing final processing for the transaction based on the status for the message and based further on whether the lock is obtained for the row. Final processing may include, for example, a transaction roll back, a transaction commit, a transaction requeue, a termination of transaction processing, or an orphaned transaction clean up.

Systems and methods of connecting users with attendees at a mega attendance event

Abstract: The technology disclosed relates to identifying and notifying a user of nearby attendees at a mega attendance event who are in user's social graph by comparing the user's social graph to a list of event attendees. The identified attendees can be stratified into social graph tags that annotate, categorize and prioritize other users in the user's social graph. The technology disclosed also relates to identifying and notifying the user of nearby attendees of sessions at the event who meet introduction preferences of the user by finding matches between introduction preference attributes specified by the user and attributes of the attendees provided by the list of event attendees.