Hybrid Tabu Search And Guided Local Search And Existence 2-Opt Local Search To Contribute In Solving The Vehicle Routing Problem With Time Windows

In this research, we are studying the possibility of contribution in solving the Vehicle Routing Problem with Time Windows(VRPTW),that is one of the optimization problems of the NP-hard type. Moreover, Hybrid algorithm (HA) provided that integrates between Tabu Search Algorithm and Guided Local Search algorithm And existence 2- Opt Local Search, based on the savings algorithm in terms of continued of a particular objective to provide a lot of savings. As we will compare the presented approach with standard tests to demonstrate the efficiency, and their impact on the quality of the solution in terms of speed of convergence and the ability to find better solutions.

Personalized Search-based Query Rewrite System for Conversational AI

Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect. User defect is caused by various reasons, such as errors in the spoken dialogue system, users' slips of the tongue or their abridged language. Many of the user defects stem from personalized factors, such as user's speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect. We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI. Our personalized QR system contains retrieval and ranking layers. Supported by user feedback based learning, training our models does not require hand-annotated data. Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.

Breadth First Reasoning Graph for Multi-hop Question Answering

Recently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. However, the unnecessary updations and simple edge constructions prevent an accurate answer span extraction in a more direct and interpretable way. In this paper, we propose a novel model of Breadth First Reasoning Graph (BFR-Graph), which presents a new message passing way that better conforms to the reasoning process. In BFR-Graph, the reasoning message is required to start from the question node and pass to the next sentences node hop by hop until all the edges have been passed, which can effectively prevent each node from over-smoothing or being updated multiple times unnecessarily. To introduce more semantics, we also define the reasoning graph as a weighted graph with considering the number of co-occurrence entities and the distance between sentences. Then we present a more direct and interpretable way to aggregate scores from different levels of granularity based on the GNN. On HotpotQA leaderboard, the proposed BFR-Graph achieves state-of-the-art on answer span prediction.