Computational Discourse Analysis

“撰写NSERC DG提案需要时间；乔的信是两个月写的。”在连贯的文本中，文本单位不是孤立地理解的，而是通过话语关系相互联系起来的，这些话语关系可能被明确标记，也可能不被明确标记。Jo在两个月内完成了她的提案，这说明编写NSERC DG应用程序需要时间！语篇分析研究试图识别和模拟语篇单位之间的连贯关系。反过来，这些又使我们能够解释文本单元的交际目的，并构建对文本整体的理解。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。语篇连贯需要语篇单位之间存在逻辑联系（例如，说明、结果、目的等）。尽管近年来有许多研究工作涉及计算语篇分析，但该领域仍处于起步阶段，许多重要的开放性问题仍然存在。本研究计划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，跨语言和跨类型的话语现象，以及话语表征在文本生成和对话系统中提高连贯性的作用。拟议的研究计划将探索三个相互关联的研究领域：领域1：建模和解开浅层和分层话语表征。区域2：分析跨语言和文本类型的话语现象。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得以发展，比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，要使这些应用程序超越单个句子，理解并产生连贯的多句文本，还需要进行大量的研究。在这个激动人心的时代，本研究计划将为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对句子产生的原因及其对之前或即将进行的交流的影响进行建模，并使我们能够构建能够自然交谈的系统。鉴于目前人工智能和NLP在加拿大的经济重要性，我相信我们的工作可以很容易地转移到加拿大的行业。

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.