Cloud solutions have gained significant popularity in the technology While there have been some studies focusing on designing effective industry as they offer a combination of services and tools to matching systems [1, 18, 20, 23, 29, 32, 35], none of these tackle specific problems. However, despite their widespread use, the works have explored the matching of cloud solutions and their customers, task of identifying appropriate company customers for a specific which holds significant business value. In Huawei Cloud, target solution to the sales team of a solution provider remains a the scenario is manual-driven, wherein our model identifies a list complex business problem that existing matching systems have of the top matching companies to the sales team associated with yet to adequately address. In this work, we study the B2B solution a specific solution. The sales team then manually reviews this list matching problem and identify two main challenges of this scenario: and proceeds with promoting the solution to those companies. This (1) the modeling of complex multi-field features and (2) the limited, specific scenario can be considered a matching problem, with the incomplete, and sparse transaction data. To tackle these challenges, primary goal being the identification of appropriate companies we propose a framework CAMA, which is built with a hierarchical (customers) for the sales teams to target in their promotion efforts.

Translation of natural language into syntactically correct commands for data visualization is an important application of natural language models and could be leveraged to many different tasks. A closely related effort is the task of translating natural languages into SQL queries, which in turn could be translated into visualization with additional information from the natural language query supplied[1]. Contributing to the progress in this area of research, we built natural language translation models to construct simplified versions of data and visualization queries in a language called Vega Zero first proposed by Luo, Yuyu, et al[2]. In this paper, we explore the design and performance of these sequence to sequence transformer based machine learning model architectures using large language models such as BERT as encoders to predict visualization commands from natural language queries, as well as apply available T5 sequence to sequence models to the problem for comparison.

As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics.

Currently, human-bot symbiosis dialog systems, e.g., pre- and after-sales in E-commerce, are ubiquitous, and the dialog routing component is essential to improve the overall efficiency, reduce human resource cost, and enhance user experience. Although most existing methods can fulfil this requirement, they can only model single-source dialog data and cannot effectively capture the underlying knowledge of relations among data and subtasks. In this paper, we investigate this important problem by thoroughly mining both the data-to-task and task-to-task knowledge among various kinds of dialog data. To achieve the above targets, we propose a Gated Mechanism enhanced Multi-task Model (G3M), specifically including a novel dialog encoder and two tailored gated mechanism modules. The proposed method can play the role of hierarchical information filtering and is non-invasive to existing dialog systems. Based on two datasets collected from real world applications, extensive experimental results demonstrate the effectiveness of our method, which achieves the state-of-the-art performance by improving 8.7\%/11.8\% on RMSE metric and 2.2\%/4.4\% on F1 metric.

The HuBERT model architecture follows the wav2vec 2.0 architecture consisting of: The number of each of these components varies between the base, large and x-large variations. Each component and its task will be better explained while explaining the training loop. The first training step consists of discovering the hidden units, and the process begins with extracting MFCCs(Mel frequency cepstrum) from the audio waveform. These are raw acoustic features useful for representing speech. Each segment of audio is then passed to the K-means clustering algorithm, and assigned to one of K clusters.

In April 2019, Benjamin Burkholder (who is awesome, by the way) published a Medium article showing off a script he wrote that uses SERP result features to infer a user's search intent. The script uses the SerpAPI.com This is one of the coolest ways to estimate search intent, because it uses Google's understanding of search intent (as expressed by the SERP features shown for that search). The one problem with Burkholder's approach is its reliance on the Serp API. If you have a large set of search queries you want to find intent for, you need to pass each query phrase through the API, which then actually does the search and returns the SERP feature results, which Burkholder's script can then classify.

Pathology reports contain useful information such as the main involved organ, diagnosis, etc. These information can be identified from the free text reports and used for large-scale statistical analysis or serve as annotation for other modalities such as pathology slides images. However, manual classification for a huge number of reports on multiple tasks is labor-intensive. In this paper, we have developed an automatic text classifier based on BERT and we propose a human-centric metric to evaluate the model. According to the model confidence, we identify low-confidence cases that require further expert annotation and high-confidence cases that are automatically classified. We report the percentage of low-confidence cases and the performance of automatically classified cases. On the high-confidence cases, the model achieves classification accuracy comparable to pathologists. This leads a potential of reducing 80% to 98% of the manual annotation workload.