Computational Discourse Analysis

计算话语分析

• 批准号: RGPIN-2020-05542
• 负责人: Kosseim, Leila
• 金额: $ 2.99万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740645
• 关键词: Computational; Discourse; Analysis

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.

动机“写一份NSERC DG建议书需要时间；乔在两个月内写好了她的建议书。”在连贯的语篇中，语篇单位不是孤立地理解的，而是通过话语关系相互联系的，这些关系可能被明确标记，也可能不被明确标记。事实上，Jo花了两个月的时间写了她的提案，这说明编写一个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。这些反过来又使我们能够解释语篇单元的交际目的，并构建对语篇整体的理解。为了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，需要能够自动识别和建模这些关系。长期客观的语篇连贯需要存在逻辑联系(例如，例证、结果、目的……)在文本单位之间。尽管近年来许多研究工作都涉及计算语篇分析，但该领域仍处于起步阶段，许多重要的悬而未决的问题仍然存在。这项研究计划的长期目标是探索语篇建模的更高级的计算方面及其在下游自然语言处理应用中的应用。我们尤其将研究层级语篇结构的建模，跨语言和跨体裁的语篇现象，以及语篇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索三个相互关联的研究领域：区域1：建模和解开浅层和层级话语表示的纠缠。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)中的使用。这项工作的意义NLP领域的惊人进展使语言个人助理等现实生活应用程序的开发以及基于深度学习的新一代研究成为可能。然而，要使这些应用超越单个句子，理解和产生连贯的多句语篇，还需要进行大量的研究。在这些激动人心的时刻，所提出的研究计划可以为自然语言处理领域做出重大贡献。在下游应用中使用语篇信息将使我们能够模拟为什么会产生一个句子，以及它对之前或即将进行的交流的影响，并将使我们能够建立能够自然对话的系统。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。