A Dataset for Sentence Retrieval for Open-Ended Dialogues

TL;DR

This paper introduces a new dataset for sentence retrieval in open-ended dialogues, enabling broader dialogue understanding beyond specific types like QA or search, and demonstrates improved neural retrieval performance using Reddit-based training data.

Contribution

The paper creates a novel dataset for sentence retrieval in open-ended dialogues from Reddit, expanding the scope beyond prior dialogue types, and proposes a weakly supervised training approach to enhance neural retrieval models.

Findings

Neural retrieval models outperform baselines on the new dataset.

Reddit-based training data improves retrieval performance.

The dataset covers diverse open-ended dialogues from Reddit.

Abstract

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.