Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Robust Zero-Shot Cross-Domain Slot Filling with Example Values

Abstract: Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting.

Multipath routing in a distributed load balancer

Abstract: A distributed load balancer in which a router receives packets from at least one client and routes packet flows to multiple load balancer nodes. The router exposes a public IP address and the load balancer nodes all advertise the same public IP address to the router. The router may implement a per-flow hashed multipath routing technique, for example an equal-cost multipath (ECMP) routing technique, to distribute the flows across the load balancer nodes. Thus, the multiple load balancer nodes may service a single public endpoint. The load balancer nodes may advertise to the router according to the Border Gateway Protocol (BGP). Rather than advertising itself, however, a load balancer node may be advertised to the router by one or more neighbor load balancer nodes; the neighbor nodes may terminate the BGP sessions with the router in response to determining that the load balancer node has failed.

Research Commentary on Recommendations with Side Information: A Survey and Research Directions

Abstract: Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.

Las palmeras de América del Sur: diversidad, distribución e historia evolutiva

Abstract: This article presents an inventory of South American palms including 457 species and 50 genera. The distribution of palms within seven phytogeographical entities is analyzed. Factors which influence the evolution of palms in South America are discussed.

Span-Level Model for Relation Extraction

Abstract: Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have been token-level models which have inherent limitations. They cannot easily define and implement span-level features, cannot model overlapping entity mentions and have cascading errors due to the use of sequential decoding. To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. We report a new state-of-the-art performance of 62.83 F1 (prev best was 60.49) on the ACE2005 dataset.