Computational Discourse Analysis

"写一份NSERC DG的提案需要时间;乔在两个月内就写好了。在连贯的文本中，文本单位不是孤立地理解的，而是通过可能有或可能没有明确标记的话语关系相互联系起来的。事实上，乔写她的建议在两个月内，说明写一个NSERC DG应用程序需要时间! 语篇分析的研究试图识别和模拟语篇单位之间的连贯关系。这些，反过来，使我们能够解释文本的单位的交际目的，并构建一个整体的文本的理解。能够自动识别和建模这些关系对于开发更自然的下游自然语言处理（NLP）应用程序（如对话系统和自然语言生成）是必要的。 长期目标语篇连贯需要逻辑联系的存在（例如，例证，结果，目的.）在文本单位之间。虽然近年来有很多研究工作涉及计算话语分析，但该领域仍处于起步阶段，许多重要的开放问题仍然存在。 该研究项目的长期目标是探索话语建模的更先进的计算方面及其在下游NLP应用中的应用。 特别是，我们将研究分层话语结构的建模，跨语言和跨体裁的话语现象，以及话语表征在文本生成和对话系统中改善连贯性的作用。科学方法拟议的研究计划将探讨三个相互关联的研究领域：区域1：建模和解开浅层次的话语表征。 领域2：分析跨语言和语篇体裁的话语现象。 领域3：探索话语建模在自然语言生成（NLG）和对话系统（DS）中的应用。NLP领域最近取得了惊人的进展，使得现实生活中的应用程序得以开发，例如支持语言的个人助理，以及基于深度学习的新一代研究。然而，这些应用程序仍然需要进行大量的研究，以超越单个句子，并理解和产生连贯的多句子文本。拟议的研究计划可以在这些激动人心的时刻为NLP领域做出重大贡献。在下游应用程序中使用话语信息将使我们能够建模为什么会产生一个句子及其对之前或即将到来的交流的影响，并使我们能够构建能够自然交谈的系统。鉴于人工智能和自然语言处理在加拿大目前的经济重要性，我相信我们的工作可以很容易地转移到加拿大的产业。

MOTIVATION "Writing an NSERC DG proposal takes time; Jo wrote hers in two months." In a coherent text, textual units are not understood in isolation but in relation with each other through discourse relations that may or may not be explicitly marked. The fact that Jo wrote her proposal in two months, illustrates that writing an NSERC DG application takes time! Research on discourse analysis tries to identify and model the coherence relations that hold between textual units. These, in turn, allow us to interpret the communicative purpose of a text's units and construct an understanding of the text as a whole. Being able to recognize and model these relations automatically is necessary to develop more natural downstream Natural Language Processing (NLP) applications such as dialogue systems and natural language generation.  LONG TERM OBJECTIVE Discourse coherence entails the existence of logical connections (for example, illustration, result, purpose...) between textual units. Although much research work has addressed computational discourse analysis in recent years, the field is still in its infancy and many important open questions still remain. The long term objective of this research program is to explore more advanced computational aspects of discourse modeling and their use in downstream NLP applications.  In particular, we will study the modeling of hierarchical discourse structures, cross-lingual and cross-genre discourse phenomena, and the role of discourse representations to improvement coherence in text generation and dialogue systems. SCIENTIFIC APPROACH The proposed research program will explore three inter-related areas of research: Area 1: Modeling and disentangling shallow and hierarchical discourse representations. Area 2: Analyzing discourse phenomena across languages and textual genres. Area 3: Exploring the use of discourse modeling for Natural Language Generation (NLG) and Dialogue Systems (DS). SIGNIFICANCE OF THE WORK Amazing recent progress in the field of NLP has allowed the development of real-life applications such as language-enabled personal assistants, as well as a new generation of research based on Deep Learning. However, much research is still required for these applications to go beyond individual sentences, and understand and produce coherent multi-sentence texts. The proposed research program can make a significant contribution to the field of NLP in these exciting times. The use of discourse information in downstream applications will allow us to model why a sentence was produced and its effect on the preceding or upcoming communication and will allow us to build systems that can converse naturally. Given the current economic importance of AI and NLP in Canada, I am confident that our work can easily be transferred to the Canadian industry.