A Survey of QUD Models for Discourse Processing (2025)

Abstract: Question Under Discussion (QUD), which is originally a linguistic analytic framework, gains increasing attention in the community of natural language processing over the years. Various models have been proposed for implementing QUD for discourse processing. This survey summarizes these models, with a focus on application to written texts, and examines studies that explore the relationship between QUD and mainstream discourse frameworks, including RST, PDTB and SDRT. Some questions that may require further study are suggested.

## introduction mainstream discourse frameworks, such as rhetorical structure theory (rst) @xcite , penn discourse treebank (pdtb) @xcite and segmented discourse representation theory (sdrt) @xcite , are based on coherence relations. these relations are typically expressed with representative lexicons, such as contrast and purpose. accordingly, discourse relation recognition is implemented as a multi-class classification task. in recent years, the question under discussion (qud) framework gains increasing attention as an alternative approach to discourse modelling, which is in line with the trend in converting nlp tasks into question-answering (qa) tasks @xcite . the qud framework is originally a linguistic analytic framework for explaining pragmatic phenomena and information structural analysis @xcite . early works by @xcite and @xcite show how the framework can be applied for discourse modelling. the main idea is that discourse units, such as sentences, can be considered as answers to some implicit or explicit questions, called quds. one qud may give rise to another qud and some quds work together to answer a higherlevel qud. the organization of discourse units can be understood by the relationship between these quds. although the conceptualization is simple, only in recent years, the implementation of the framework becomes a popular topic in discourse annotation and parsing. one reason is that reconstructing implicit quds for discourse units has been deemed an infeasible task because of the lack of constraints in the question generation process @xcite . the second reason is that justification is needed for the reconstructed implicit quds, which makes the approach less favorable for analyzing written texts, where quds are implicit in most of the cases @xcite . nevertheless, using freeform questions for discourse annotation is arguably simpler for lay annotators than using a fixed set of discourse relations predefined by experts, which are often abstract and ambiguous. moreover, natural language generation (nlg) and qa tasks have been spurred by the development of large language models (llms), making the qud approach to discourse modelling a potentially more cost-effective option than the other frameworks @xcite . a few challenges are discernible with implementing the qud framework for discourse modelling. similar to discourse frameworks based on coherence relations such as rst and pdtb, models that vary in the underlying theoretical assumptions have been proposed under the qud framework, such as the qud-tree approach by @xcite and de @xcite and the expectation-driven approach by @xcite . as these models are typically rooted in linguistic theories, it is difficult to understand the constraints and the properties of the representations obtained with the models. meanwhile, the evaluation of qud annotation and the automatic generation of quds are challenging due to the use of free-form questions, since a single qud can be expressed in multiple ways. in this paper, we survey models for discourse processing proposed under the qud framework to support researchers who are interested in applying this framework in downstream tasks or exploring discourse annotation and processing with the qud framework @xcite , or integrating different discourse frameworks @xcite . compared with mainstream discourse frameworks, the body of literature on qud is much smaller, particularly regarding the implementation. the studies covered in this survey were selected based on their influence in the field of computational linguistics, as reflected by follow-up works inspired by them. we began with seminal and high-impact studies and traced their references to develop a comprehensive understanding of the research landscape. the contributions of this paper are summarized as follows: ## qud-based discourse models two general approaches to discourse processing can be identified within the qud framework: the qud-tree approach and the expectation-driven approach. the first approach is based on the theoretical proposal by @xcite , which uses trees to model the structure of quds. the second approach, named as such in the study by @xcite , is developed by @xcite . in addition to the two canonical approaches, @xcite introduce another approach, which features inquisitive questions that can be anchored in the previous context (henceforth "dependency-based qud"). this approach is similar to the expectationdriven approach in some ways but involves the identification of the source that triggers the question in the previous context. ## benchmarks and datasets as pointed out by @xcite , the reconstruction of quds typically does not rely on morphosyntactic signals, making the framework potentially cross-linguistically applicable. works have shown that qud is applicable to languages other than english, such as german @xcite and italian @xcite . similar to other mainstream discourse frameworks, such as rst and pdtb, qud can be used for capturing higher-level linguistic information. as such, it is potentially useful for various nlp tasks that involve higher-level linguistic understanding. applications already explored include facilitating narrative understanding @xcite , where the dependency-based approach is adopted, conditional text generation @xcite , which does not involve implementation of any of the existing qud frameworks but only turns sentential information into question and answer pairs, similar to the first step of automatic question generation adopted by de kuthy et al. ( 2020 ), text simplification, specifically elaborative simplification @xcite , and summarization @xcite . ## relationship between qud and discourse frameworks this section reviews existing studies that explore the relationship between qud and mainstream discourse frameworks including rst, pdtb, and sdrt 4.1 qud and rst @xcite is a corpus containing salience ratings and their natural language rationales for 1,766 inquisitive questions. 3. regarding evaluation of quds: qude-val @xcite is a dataset comprising fine-grained evaluation of 2,190 quds over 51 news articles. ## conclusion in this survey, three approaches for implementing the qud framework for discourse representation are identified. similar to the case with mainstream discourse frameworks represented by rst, pdtb and sdrt, these approaches exhibit varying focuses and capture different types of discourse information. while there are a few studies that explore the relationship between qud-based discourse models and mainstream discourse frameworks, it remains an under-studied question whether quds represent the same type of discourse information as that encoded by discourse relations, for example, if why questions encode causal relations consistently across the different frameworks and when why questions are used for eliciting a background relation (if possible). research on this question may provide insights on the strengths and weaknesses of different discourse frameworks. moreover, it is still challenging to achieve high interannotator agreement, especially with the qud-tree approach. in future work, a two-step approach similar to the method by @xcite can be adopted to control the qud annotation process, where quds are elicited first and then categorized based on a predefined set of question templates that can be mapped unambiguously to discourse relations @xcite . additionally, automatic qud generation and application in downstream tasks also require further research.