Grounding in social media: An approach to building a chit-chat dialogue model

Ritvik Choudhary, Daisuke Kawahara
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引用次数: 2

Abstract

Building open-domain dialogue systems capable of rich human-like conversational ability is one of the fundamental challenges in language generation. However, even with recent advancements in the field, existing open-domain generative models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. Current work on knowledge-grounded dialogue generation primarily focuses on persona incorporation or searching a fact-based structured knowledge source such as Wikipedia. Our method takes a broader and simpler approach, which aims to improve the raw conversation ability of the system by mimicking the human response behavior through casual interactions found on social media. Utilizing a joint retriever-generator setup, the model queries a large set of filtered comment data from Reddit to act as additional context for the seq2seq generator. Automatic and human evaluations on open-domain dialogue datasets demonstrate the effectiveness of our approach.
社交媒体的基础:建立闲聊对话模型的方法
构建具有丰富的类人对话能力的开放域对话系统是语言生成的基本挑战之一。然而,即使在该领域取得了最新进展,现有的开放域生成模型也无法捕获和利用外部知识,从而导致对未见过的话语的重复或通用响应。目前关于基于知识的对话生成的工作主要集中在人物角色合并或搜索基于事实的结构化知识来源,如Wikipedia。我们的方法采用了一种更广泛、更简单的方法,旨在通过在社交媒体上发现的随意互动来模仿人类的反应行为,从而提高系统的原始对话能力。利用联合检索器-生成器设置,该模型从Reddit查询大量过滤的评论数据,作为seq2seq生成器的附加上下文。对开放域对话数据集的自动和人工评估证明了我们方法的有效性。
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