CRII: RI: Alignment in Web-Forum Discourse: Computational Models of Adaptation and Language Change

真实世界对话中的语言使用是在语境中发生的。 语言的选择取决于先前的选择：例如，所选择的单词和句子结构往往反映了会话伙伴先前使用的内容。这种微妙的适应过程被称为“对齐”。对齐似乎有助于人们在对话中相互理解，它似乎也延伸到人机界面。 然而，在对话中协调一致的具体功能并不明确。这仅仅是人类记忆工作原理的一个有用的附带现象吗？它是否作为一种社交或交流信号？这是一个人同情心的表现吗？它是否有助于社区在很长一段时间内找到共同语言？ 最近的研究已经确定，对齐的后果之一是个人的持续语言变化。 也有初步证据表明，随着时间的推移，人们彼此交谈的群体将在他们的词汇和句子结构的选择趋同。 换句话说，他们找到了共同的语言。 该项目将设计描述和量化这些过程的计算模型。 有了这些，人们可以在实际的语言使用中发现它们，例如在网络论坛中。 事实上，该项目将使用数十年网络论坛信息的大数据集来生成这些模型。 计算模型将以一种可在现代社交网络和数据科学中利用的方式解释和预测过程。 考虑一个网络论坛的例子，它将那些患有疾病的人联系起来，这样他们就可以互相提供情感和信息支持。 这些模型可以检测和预测这个网络论坛中的哪些消息在预期水平上最具支持性，以及它们是否与提出问题的人一致。 这一点的一个可能的应用可以通过对搜索结果进行优先排序和提出阅读建议来改善网络论坛的话语。 比对模型还可以通过发现相互支持者的网络来改进大型数据集的分析技术。 将创建模型，以描述和解释自然语言对话中的对齐和语言变化。这些模型将是计算和统计的，以允许利用自然语言对话中的交互对齐作为社交网络应用程序的一个功能。统计对齐模型将语言随时间的变化描述为表征个体行为、记忆和网络信息的变量的函数。 这些模型将适合纵向数据集来自基于网络的，面向主题的会话线程。 在个人层面上，它们将有助于完善语言产生中记忆功能的认知计算模型，这将受到有效的ACT-R框架的约束。基于语料库的句法启动和ACT-R语言生产模型的初步工作，并在语料库中对齐的试点实验的可行性的方法是支持的。 该项目的结果可能指向优先排序和过滤最有帮助的内容的新方法，并且可以解决患者的生活质量和福祉，例如同行支持社区的那些患者，他们的对话在研究者的工作中进行了研究，以激励提案。

Language use in real-world dialogue happens in context. Linguistic choices depend on previous ones: for example, the chosen words and sentence structures tend to mirror what was used previously by a conversation partner. This subtle adaptation process has been called "alignment". Alignment appears to help people understand each other in dialogue, and it seems to extend to human-computer interfaces, too. The concrete functions of alignment in dialogue are, however, unclear. Is it merely a useful epiphenomenon of how human memory works? Does it serve as a social or communicative signal? Is it indicative of a person's empathy? Does it help communities find a common language over long periods of time? Recent work has established that one of the consequences of alignment is persistent language change in the individual. There also is preliminary evidence that over time, groups of people talking to one another will converge in their choice of words and sentence structure. In other words, they find a common language. The project will devise computational models that describe and quantify these processes. With these, one can detect them in actual language use, such as in web-forums. In fact, the project will use big datasets from decades of web-forum messages to produce those models. The computational models will explain and predict processes in a way that makes them exploitable in modern social networks as well as for data science. Consider the example of a web-forum that connects those suffering from a disease so they can lend each other emotional and informational support. The models can detect and predict which messages in this web-forum are most supportive on the intended level, and whether they align to the person asking a question. A possible application of this may improve web-forum discourse by prioritizing search results and by making reading suggestions. Alignment models may also improve analysis techniques for large datasets by spotting networks of mutual supporters. Models will be created in order to describe and explain alignment and language change in natural-language dialogue. The models will be computational and statistical to allow for exploitation of interactive alignment in natural-language dialogue as a feature in social network applications. Statistical alignment models describe language change over time as a function of variables that characterize the individual's behavior, memory, and of network information. These models will be fitted to longitudinal datasets derived from web-based, topic-oriented conversation threads. At the individual level, they will help refine cognitive-computational models of memory function in language production, which will be constrained by the well-validated ACT-R framework. The viability of the approach is supported by preliminary work on corpus-based syntactic priming and ACT-R models of language production, and pilot experiments showing alignment in the corpus. The outcomes of the project may point to novel methods of prioritizing and filtering the most helpful content and can address quality of life and well-being of patients such as those of the peer-support community whose conversations were studied in the investigator's work motivating the proposal.