探索不同對話任務的重寫方法

摘要

對話式助手通常需要一種問題重寫算法，該算法利用過往互動的子集來為用戶的問題或請求提供更為精確（準確）的答案。然而，具體的重寫方法往往取決於對話助手所支持的用例和應用特定任務，以及其他限制條件。在本文中，我們系統地研究了兩種不同的方法，分別稱為重寫和融合，並將其應用於兩種根本不同的生成任務，包括一個文本到文本的生成任務和一個多模態生成任務，後者以文本為輸入並生成可視化或數據表來回答用戶的問題。我們的結果表明，具體採用重寫還是融合方法高度依賴於底層的用例和生成任務。特別是，我們發現對於對話式問答助手，查詢重寫方法表現最佳，而對於基於用戶與助手對話生成可視化和數據表的數據分析助手，融合方法效果最好。值得注意的是，我們針對數據分析助手的用例探索了兩個數據集，分別對應短對話和長對話，我們發現查詢融合始終表現更佳，而對於基於文本的對話式問答，查詢重寫方法表現最佳。

English

Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's question or request. However, the exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant, among other constraints. In this paper, we systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks, including a text-to-text generation task and a multimodal generative task that takes as input text and generates a visualization or data table that answers the user's question. Our results indicate that the specific rewriting or fusion approach highly depends on the underlying use case and generative task. In particular, we find that for a conversational question-answering assistant, the query rewriting approach performs best, whereas for a data analysis assistant that generates visualizations and data tables based on the user's conversation with the assistant, the fusion approach works best. Notably, we explore two datasets for the data analysis assistant use case, for short and long conversations, and we find that query fusion always performs better, whereas for the conversational text-based question-answering, the query rewrite approach performs best.

探索不同對話任務的重寫方法

Exploring Rewriting Approaches for Different Conversational Tasks

摘要

Summary

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