# Neural Contextual Conversation Learning with Labeled Question-Answering Pairs Source: [Neural Contextual Conversation Learning with Labeled Question-Answering Pairs]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:22:26.903Z Authors: Kun Xiong, Anqi Cui, Zefeng Zhang, Ming Li Published: 2016-07-20T03:25:31.000Z ## Abstract Neural conversational models tend to produce generic or safe responses in different contexts, e.g., reply \\textit{"Of course"} to narrative statements or \\textit{"I don't know"} to questions. In this paper, we propose an end-to-end approach to avoid such problem in neural generative models. Additional memory mechanisms have been introduced to standard sequence-to-sequence (seq2seq) models, so that context can be considered while generating sentences. Three seq2seq models, which memorize a fix-sized contextual vector from hidden input, hidden input/output and a gated contextual attention structure respectively, have been trained and tested on a dataset of labeled question-answering pairs in Chinese. The model with contextual attention outperforms others including the state-of-the-art seq2seq models on perplexity test. The novel contextual model generates diverse and robust responses, and is able to carry out conversations on a wide range of topics appropriately. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()