Discourse Sense Flows: Modelling the Rhetorical Style of Documents across Various Domains (2023)

Abstract: Recent research on shallow discourse parsing has given renewed attention to the role of discourse relation signals, in particular explicit connectives and so-called alternative lexicalizations. In our work, we first develop new models for extracting signals and classifying their senses, both for explicit connectives and alternative lexicalizations, based on the Penn Discourse Treebank v3 corpus. Thereafter, we apply these models to various raw corpora, and we introduce ‘discourse sense flows’, a new way of modeling the rhetorical style of a document by the linear order of coherence relations, as captured by the PDTB senses. The corpora span several genres and domains, and we undertake comparative analyses of the sense flows, as well as experiments on automatic genre/domain discrimination using discourse sense flow patterns as features. We find that n-gram patterns are indeed stronger predictors than simple sense (unigram) distributions.

## introduction people write differently, and the style of writing highly depends on the author's intention. while texts from the news are largely informative, an argumentative essay, for example, is written to support a particular opinion. understanding these underlying purposes and structures in a text-or why the text is arranged as it is-is a complex problem and requires giving more attention to differences between genres (e.g., news versus review) and domains (e.g., economics versus science). previous research has examined some genre differences within a single newspaper corpus @xcite , and more recent work has shown the impact of joining diverse genres in training data for discourse parsing @xcite . similarly, @xcite demonstrated that it is difficult to generalize "discourse knowledge" from one domain to others, e.g., from news to biomedical text. however, there is so far not much work on genre/domain differentiation on the basis of discourse features for large amounts of text documents. we address this task from the perspective of discourse relations as operationalized in the penn discourse treebank (pdtb) @xcite . it represents discourse relations in the form of two text spans (arguments: arg1 and arg2) that are associated with a sense. these senses are organized in a three-level hierarchy and most research focuses on the first two levels, coarse and fine (see in our work, we for now rely only on the discourse signals and do not consider the arguments. here is an example from the pdtb corpus with three signals: the pdtb distinguishes two groups of signaled relations: explicit relations are signaled by a closed set of discourse connectives, e.g. because, and, if-then, and before. alternative lexicalizations (altlex) use phrases other than connectives, e.g. to begin with, for that reason, and it all adds up to. while the first two signals in ex. (1) are explicit signals (and their senses are temporal.asynchronous and expansion.conjunction, respectively), the last one represents an alternative lexicalization with sense expansion.instantiation. in total, the pdtb (version 3) contains 25,878 signaled relations, 94% of which are of type 'explicit', and the rest are altlex phrases. importantly, the pdtb also contains 'implicit' relations that are not lexically signaled at all; for our present purposes, we do not use them. the introduction of email and text messaging has brought about considerable changes in communication. with this situation, some people believe that they may be factors decreasing the position of written language. i am in favor of that idea because of their advantages and the fact we can see today about the use of these new means. first of all, it is easy to point out many benefits users can get from email and text messaging, which answers the question of their great popularity. before email and mobile phones, human beings communicated by meeting directly, sending letters, or later, calling from home phones. such ways usually made people have trouble for the expensiveness, difficulties in far communication or emergency, and the loss of information. however, email and cell phones have improved the obstacles above. people can send or receive electronic letters anywhere and anytime they want. especially, thanks to the function provided by email and text messaging. the introduction of email and text messaging has brought about considerable changes in communication. with this situation, some people believe that they may be factors decreasing the position of written language. i am in favor of that idea because of their advantages and the fact we can see today about the use of these new means. first of all, it is easy to point out many benefits users can get from email and text messaging, which answers the question of their great popularity. before email and mobile phones, human beings communicated by meeting directly, sending letters, or later, calling from home phones. such ways usually made people have trouble for the expensiveness, difficulties in far communication or emergency, and the loss of information. however, email and cell phones have improved the obstacles above. people can send or receive electronic letters anywhere and anytime they want. especially, thanks to the function provided by email and text messaging. in this paper, we develop the first models for identifying explicit and altlex signals and their senses from the recent version 3 of the pdtb. then, we use the models on corpora of raw texts from different domains and genres, and we demonstrate that automatically produced signal/sense information can be used to differentiate those domains. figure 1 illustrates the steps from raw documents to 'sense profiles' that we regard as models of the document's rhetorical style. they are operationalized by the distributions of n-gram patterns extracted from the document's sense flow. ## data this work examines the different rhetorical styles found in documents and investigates whether documents can be associated with their corresponding corpus, in the following referred to as domain, and also whether different domains have certain patterns in common. therefore, we prepare additional corpora, summarized in the penn discourse treebank (pdtb) @xcite consists of 2,499 stories from a threeyear wall street journal (wsj) collection, and thus belongs to the genre of news articles. we use this corpus for training as it is by far the largest available source for discourse relation annotations. while the previous version contains around 43k annotations with 18.4k explicit and 16k implicit relations, the recent version 3 introduces a new annotation scheme with a revised sense hierarchy as well as 13k additional relations. in the following, we briefly summarize our additional raw corpora grouped according to their genre. news the new york times annotated corpus (nyt) 1 contains about 1.8 million documents published by the new york times between 1987 and 2007. we sample 500 documents per month from the years 2000-2007, but remove some of the articles due to peripheral contents, e.g., business market overviews, and thus focus on the more recent part of the dataset. the bbc news articles (bbc) @xcite consists of 2225 documents from the bbc news website corresponding to stories in five topical areas (business, entertainment, politics, sport, tech) from 2004-2005. essays the automated essay scoring corpus (aes) foot_1 was created for a shared task by the hewlett foundation in 2012. this corpus consists of manually-scored student essays for different prompts and grade levels. for our study, we selected only the 1,500 essays of the persuasive task written by 10th-grade students. the ukp argument annotated essays corpus (pec) @xcite consists of 402 persuasive essays written by language learners (crawled from an essay website). speeches the ted conferences transcripts (ted) foot_2 is a collection of english transcripts crawled from the ted site. it contains about 2500 talks with publication years ranging from 2006 to 2017. we add artificial paragraph breaks whenever audience information such as applause was noted. the un security council debates corpus (un) @xcite is based on 5,748 meeting protocols dated between 1995 and 2020. the protocols are split into distinct speeches and result in 82,165 individual documents. documents from 2018-2020 have been removed due to some conversion errors in the dataset. we randomly select 5000 documents from the corpus. ## identification of signaled discourse relations for our study, we train models for both tasks, identification of discourse signals and classifying their sense, separately. similar to kurfalı and östling (2021), for better context information we process full paragraphs instead of individual sentences. for signal labeling, we use a pre-trained large language model (llm) and fine-tune the base model combined with a linear transformation and a conditional random field (crf) @xcite as output layer. for properly encoding discourse signals, we use bioes encoding. this has the advantage that we can encode all types of discourse signals without loss: discontinuous signals such as not only [. . . ] but also [. . . ] and also signals that are nested within each other. we further add start and end tags for technical reasons related to the crf. for relation sense identification, we follow most previous research and focus on the second level rather than on the coarse senses. due to the small amount of samples available for some classes, we ignore those that are distinguished by the features negation, speech act, and belief. ## conclusions we introduced the first models for identifying both explicit and altlex signals and their senses in the pdtb v3 corpus. the resulting system was then used on unseen corpora from different domains, and we propose the notion of discourse sense flows to model the rhetorical style of documents. reducing complete flows to n-grams of different sizes enables us to discriminate certain genres and domains with a classifier that uses merely these automatically generated n-grams as features. our work is currently limited to discourse relations that contain lexical signals, e.g., explicit connectives and alternative lexicalizations. however, given a classifier for implicit relations (a task that received much attention in the literature in recent years), those relations can be integrated, and we assume this can lead to more explanatory sense flows and thereby to improved genre/domain classification performance. in addition, we anticipate further applications of discourse sense flows, for example in essay scoring or in text-level sentiment analysis. finally, assuming the availability of robust rststyle parsing, adapting the extraction of discourse sense flows to tree structures might be useful, either by linearizing the tree or extracting more complex tree patterns than sense n-grams.