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我們研究會話領域探索(CODEX),其中用戶的目標是通過與信息機器人交談來豐富她對給定領域的知識。這樣的對話應該以高質量的領域知識為基礎,同時也要有吸引力和開放性。食典委機器人應積極主動地介紹相關信息,即使用戶沒有直接要求。機器人還應該適當地將對話轉向域的未發現區域。為了解決這些對話特性,我們引入了一種稱為動態組合的新方法,該方法將候選內容生成與機器人響應的靈活組合解耦。這允許機器人控製所提供內容的來源、正確性和質量,同時通過對話管理器以組合方式選擇最合適的內容來實現靈活性。我們實現了一個基於動態組合的法典機器人,並將其集成到穀歌助理中。作為一個示例域,該機器人以無縫體驗的方式談論NBA籃球聯賽,因此用戶不知道他們是在與vanilla係統交談,還是在與我們的CODEX機器人進行交談。結果是積極的,並能讓你洞悉怎樣才能進行一次愉快的談話。據我們所知,這是作為商業助理係統一部分的開放式對話的第一次真正的用戶實驗。

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最新論文

Recent advancement of the WWW, IOT, social network, e-commerce, etc. have generated a large volume of data. These datasets are mostly represented by high dimensional and sparse datasets. Many fundamental subroutines of common data analytic tasks such as clustering, classification, ranking, nearest neighbour search, etc. scale poorly with the dimension of the dataset. In this work, we address this problem and propose a sketching (alternatively, dimensionality reduction) algorithm -- $\binsketch$ (Binary Data Sketch) -- for sparse binary datasets. $\binsketch$ preserves the binary version of the dataset after sketching and maintains estimates for multiple similarity measures such as Jaccard, Cosine, Inner-Product similarities, and Hamming distance, on the same sketch. We present a theoretical analysis of our algorithm and complement it with extensive experimentation on several real-world datasets. We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking. Our proposed algorithm offers a comparable accuracy while suggesting a significant speedup in the dimensionality reduction time, with respect to the other candidate algorithms. Our proposal is simple, easy to implement, and therefore can be adopted in practice.

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