Ghosting, the ability to predict a user's intended text input for inline query auto-completion, is an invaluable feature for modern search engines and chat interfaces, greatly enhancing user experience. By suggesting completions to incomplete queries (or prefixes), ghosting aids users with slow typing speeds, disabilities, or limited language proficiency. Ghosting is a challenging problem and has become more important with the ubiquitousness of chat-based systems like ChatGPT, Copilot, etc. Despite the increasing prominence of chat-based systems utilizing ghosting, this challenging problem of Chat-Ghosting has received little attention from the NLP/ML research community. There is a lack of standardized benchmarks and relative performance analysis of deep learning and non-deep learning methods. We address this through an open and thorough study of this problem using four publicly available dialog datasets: two human-human (DailyDialog and DSTC7-Ubuntu) and two human-bot (Open Assistant and ShareGPT). We experiment with various existing query auto-completion methods (using tries), n-gram methods and deep learning methods, with and without dialog context. We also propose a novel entropy-based dynamic early stopping strategy. Our analysis finds that statistical n-gram models and tries outperform deep learning based models in terms of both model performance and inference efficiency for seen prefixes. For unseen queries, neural models like T5 and Phi-2 lead to better results. Adding conversational context leads to significant improvements in ghosting quality, especially for Open-Assistant and ShareGPT. We make code and data publicly available

翻译：Ghosting（幽灵输入）是一种能够预测用户意图文本输入以实现行内查询自动补全的技术，对于现代搜索引擎和聊天界面而言，这项功能通过显著提升用户体验而具有不可估量的价值。通过对不完整查询（或前缀）提供补全建议，ghosting能够帮助打字速度较慢、存在身体障碍或语言能力有限的用户。Ghosting是一个具有挑战性的问题，随着ChatGPT、Copilot等基于聊天的系统日益普及，其重要性愈发凸显。尽管采用ghosting技术的聊天系统日益增多，但Chat-Ghosting这一难题在自然语言处理/机器学习研究领域尚未获得足够关注。目前缺乏标准化的基准测试，也缺少对深度学习方法与非深度学习方法的相对性能分析。为此，我们利用四个公开对话数据集对该问题展开了开放且深入的研究：其中两个为人-人对话数据集（DailyDialog和DSTC7-Ubuntu），两个为人-机器对话数据集（Open Assistant和ShareGPT）。我们试验了多种现有查询自动补全方法（基于字典树）、n-元文法方法及深度学习方法，并分别考察了包含与不包含对话上下文的情况。此外，我们提出了一种基于熵的动态早停策略。分析结果表明：对于已见前缀，统计n-元文法模型和字典树在模型性能与推理效率方面均优于基于深度学习的模型；而对于未见查询，T5和Phi-2等神经模型能产生更优结果。添加对话上下文可显著提升ghosting质量，在Open-Assistant和ShareGPT数据集上表现尤为明显。我们已公开相关代码与数据。