论文标题
开放式对话的句子检索数据集
A Dataset for Sentence Retrieval for Open-Ended Dialogues
论文作者
论文摘要
我们解决了开放式对话的句子检索任务。目的是从文档语料库中检索句子，该句子包含有助于在给定对话中生成下一回合的信息。基于对话的检索的先前工作重点是特定类型的对话：对话质量质量检查或对话搜索。为了解决可以使用任何类型的对话的更广泛的范围，我们构建了一个数据集，其中包括Reddit的开放式对话，Wikipedia的候选句子，每次对话和句子的人类注释。我们报告了数据集上几种检索基线的性能，包括神经检索模型。为了使神经模型适应数据集中的对话类型，我们探索了一种诱导Reddit的大规模弱监督培训数据的方法。使用此训练集可显着改善MS MARCO数据集上的培训的性能。
We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conversational search. To address a broader scope of this task where any type of dialogue can be used, we constructed a dataset that includes open-ended dialogues from Reddit, candidate sentences from Wikipedia for each dialogue and human annotations for the sentences. We report the performance of several retrieval baselines, including neural retrieval models, over the dataset. To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. Using this training set significantly improved the performance over training on the MS MARCO dataset.
