Penn Discourse Treebank Research Articles

ERST: A Signaled Graph Theory of Discourse Relations and Organization

Abstract In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory (SDRT), the Penn Discourse Treebank (PDTB) and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics and applications for data in our framework.

Semantic Augmentation in Chinese Adversarial Corpus for Discourse Relation Recognition Based on Internal Semantic Elements

This paper proposes incorporating linguistic semantic information into discourse relation recognition and constructing a Semantic Augmented Chinese Discourse Corpus (SACA) comprising 9546 adversative complex sentences. In adversative complex sentences, we suggest a quadruple (P, Q, R, Qβ) representing internal semantic elements, where the semantic opposition between Q and Qβ forms the basis of the adversative relationship. P denotes the premise, and R represents the adversative reason. The overall annotation approach of this corpus follows the Penn Discourse Treebank (PDTB), except for the classification of senses. We combined insights from the Chinese Discourse Treebank (CDTB) and obtained eight sense categories for Chinese adversative complex sentences. Based on this corpus, we explore the relationship between sense classification and internal semantic elements within our newly proposed Chinese Adversative Discourse Relation Recognition (CADRR) task. Leveraging deep learning techniques, we constructed various classification models and the model that utilizes internal semantic element features, demonstrating their effectiveness and the applicability of our SACA corpus. Compared with pre-trained models, our model incorporates internal semantic element information to achieve state-of-the-art performance.

The Thai Discourse Treebank: Annotating and Classifying Thai Discourse Connectives

Abstract Discourse analysis is a highly applicable area of natural language processing. In English and other languages, resources for discourse-based tasks are widely available. Thai, however, has hitherto lacked such resources. We present the Thai Discourse Treebank, the first, large Thai corpus annotated in the style of the Penn Discourse Treebank. The resulting corpus has over 10,000 sentences and 18,000 instances of connectives in 33 different relations. We release the corpus alongside our list of 148 potentially polysemous discourse connectives with a total of 340 form-sense pairs and their classification criteria to facilitate future research. We also develop models for connective identification and classification tasks. Our best models achieve an F1 of 0.96 in the identification task and 0.46 on the sense classification task. Our results serve as benchmarks for future models for Thai discourse tasks.

Toward a shallow discourse parser for Turkish

Abstract One of the most interesting aspects of natural language is how texts cohere, which involves the pragmatic or semantic relations that hold between clauses (addition, cause-effect, conditional, similarity), referred to as discourse relations. A focus on the identification and classification of discourse relations appears as an imperative challenge to be resolved to support tasks such as text summarization, dialogue systems, and machine translation that need information above the clause level. Despite the recent interest in discourse relations in well-known languages such as English, data and experiments are still needed for typologically different and less-resourced languages. We report the most comprehensive investigation of shallow discourse parsing in Turkish, focusing on two main sub-tasks: identification of discourse relation realization types and the sense classification of explicit and implicit relations. The work is based on the approach of fine-tuning a pre-trained language model (BERT) as an encoder and classifying the encoded data with neural network-based classifiers. We firstly identify the discourse relation realization type that holds in a given text, if there is any. Then, we move on to the sense classification of the identified explicit and implicit relations. In addition to in-domain experiments on a held-out test set from the Turkish Discourse Bank (TDB 1.2), we also report the out-domain performance of our models in order to evaluate its generalization abilities, using the Turkish part of the TED Multilingual Discourse Bank. Finally, we explore the effect of multilingual data aggregation on the classification of relation realization type through a cross-lingual experiment. The results suggest that our models perform relatively well despite the limited size of the TDB 1.2 and that there are language-specific aspects of detecting the types of discourse relation realization. We believe that the findings are important both in providing insights regarding the performance of the modern language models in a typologically different language and in the low-resource scenario, given that the TDB 1.2 is 1/20th of the Penn Discourse TreeBank in terms of the number of total relations.

Lithuanian Discourse Markers And Their Relations In A Multilingual Corpus

Summary The development and research of discourse-annotated corpora is a relatively new field, therefore Lithuanian researchers seek to supplement the existing corpora resources and look for ways to study discourse relations by linking and comparing them with their counterparts in other languages because in different languages discourse relations are realized by different linguistic means. The aim of the article is to present the developing available corpora resources drawing on the experience of foreign scholars and to discuss guidelines for translation research at the discourse level. Therefore, the article first deals with the possibilities of expressing discourse relations by using discourse markers as their linguistic realization in different languages, discussing possible choices of translators, taking into account discourse realtions in translation and the use of different linguistic means. The article presents the parallel multilingual corpus TED-MBD (Multilingual discourse-annotated corpus), which is annotated at the discourse level, in accordance with the objectives and principles of PDTB (Penn Discourse Treebank) discourse annotation. The article discusses in detail the annotation system of PDTB discourse markers, the reader is introduced to the hierarchy of senses of discourse relations, the principles of annotation and insights into the application of the PDTB scheme. It also describes the annotation principles of the Lithuanian part of the corpus in accordance with the PDTB discourse annotation rules; the first results related to the expression of discourse relations and the use of discourse markers are discussed. The article also presents the first research guidelines on how to compare Lithuanian and English discourse-annotated texts in order to understand translation tendencies at the discourse level.

Explicitness and implicitness of discourse relations in a multilingual discourse bank

Abstract Proposals such as continuity and causality-by-default relate the level of expectedness of a relation to its linguistic marking as an explicit or implicit relation. We investigate these two proposals with regard to the English transcripts of six TED Talks and their Lithuanian, Portuguese and Turkish translations in the TED-Multilingual Discourse Bank (TED-MDB), annotated for discourse relations, following the Penn Discourse Treebank style of annotation. Our data shows that the discontinuous relations contrast and concession are indeed frequently explicit in all languages. But continuous relations show differences per relation and language. For instance, cause is frequently conveyed implicitly in English and Portuguese, but not in Lithuanian and Turkish. We explore temporal continuity by analysing whether the forward-order sense result is more frequently implicit than the backward-order reason. The hypothesis is confirmed by English and Portuguese, but not Lithuanian and Turkish. However, in Turkish, the arguments of the backward-order relation reason are frequently presented by the reversed order of arguments, retaining the linear order of events even in the presence of the connective. The causality-by-default hypothesis is not confirmed, as cause is not the most frequent implicit relation in the four languages.

Usage disambiguation of Turkish discourse connectives

This paper describes a rule-based approach and a machine learning approach to disambiguate the discourse usage of Turkish connectives, which not only has single and phrasal connectives as most languages do, but also suffixal connectives that largely correspond to subordinating conjunctions in English. Since these connectives have different linguistic characteristics, two sets of linguistic rules are devised to disambiguate their discourse usage. The linguistic rules are used in the rule-based approach and employed as feature sets in the machine learning approach to test whether they influenced the decision of our algorithms. The results of both approaches are evaluated over the Turkish section of TED-Multilingual Discourse Bank and Turkish Discourse Bank 1.1, two datasets annotated in the Penn Discourse TreeBank style. The paper attests to the predictive power of the linguistic rules in disambiguating the discourse usage of both types of connectives also offering new knowledge and insights for discourse processing from the view of a morphologically rich language.

Analyzing Chinese text with clause relevance structure

Discourse structure is generally represented as hierarchical structure, the two most well known representations are rhetorical structure theory (RST) and Penn discourse treebank (PDTB). The main problem of the hierarchical structure is that it can not describe the direct semantic relevance between the elementary discourse units (EDU), especially the non-adjacent and cross-level EDUs. Discourse dependency structure (DDS) has been put forward in recent years to describe the head-dependent relation between the EDUs. However, the judgment process of the head can not be answered theoretically. This problem is particularly serious in Chinese discourse analysis, because Chinese lacks the form differences between the main clauses and the subordinate clauses. In this paper, we propose clause relevance structure to represent the discourse structure. Compared with the hierarchical discourse structure and DDS, the clause relevance structure can effectively describe the direct semantic association between discontinuous and cross-level clauses in a text, and the construction of structure is not presupposed by the head recognition. We propose the judgment criteria and formal constraints of the clause relevance structure, and built a human-annotated corpus on Chinese text. Based on the Chinese corpus, we explore the automatic recognition of clause relevance structure. The clause relevance recognition task is formalized as a classification problem and performed by the BERT-based model. A bidirectional LSTM layer is added on the top of the BERT to improve the performance, and the recognition accuracy (90.77%) is achieved by the BERT-LSTM model. Experimental results show that the long distance clause pairs are the main difficulties in the clause relevance recognition, and these difficulties mainly focus on the positive examples, while the clause pairs with short distance are especially difficult to be correctly recognized as negative relevance.

Eksplicytność a implicytność w świetle analizy korpusowej (meta)tekstu

The article analyzes differences in the description of discourse relations in corpus research, in particular with the reference to the use of discourse markers – expressions that tie together subsequent fragments of the text and provide information about the nature of these relations. The text presents three concepts of the description of explicitness and implicitness of the content: Rhetorical Structure Theory, Penn Discourse Treebank and the author’s original proposal and indicates the consequences of each solution. The analysis of relations with particles as metatexual expressions defined in accordance with The Nest Dictionary of Polish reveals the possibility of expressing explicitness as a representation of elements of informational structure shaped by the use of a given particle, and implicitness as a lack of representation of certain elements of this type.

Anaphoric Connectives and Long-Distance Discourse Relations in Czech

This paper is a linguistic as well as technical survey for the development of a shallow discourse parser for Czech. It focuses on long-distance discourse relations signalled by (mostly) anaphoric discourse connectives. Proceeding from the division of connectives on “structural” and “anaphoric” according to their (in)ability to accept distant (non-adjacent) text segments as their left-sided arguments, and taking into account results of related analyses on English data in the framework of the Penn Discourse Treebank, we analyze a large amount of language data in Czech. We benefit from the multilayer manual annotation of various language aspects from morphology to discourse, coreference and bridging relations in the Prague Dependency Treebank 3.0. We describe the linguistic parameters of long-distance discourse relations in Czechin connection with their anchoring connective, and suggest possible ways of their detection. Our empirical research also outlines some theoretical consequences for the underlying assumptions in discourse analysis and parsing, e.g. the risk of relying too much on different (language-specific?) part-of-speech categorizations of connectives or the different perspectives in shallow and global discourse analyses (the minimality principle vs. higher text structure).

Focus-sensitive relation disambiguation for implicit discourse relation detection

We study implicit discourse relation detection, which is one of the most challenging tasks in the field of discourse analysis. We specialize in ambiguous implicit discourse relation, which is an imperceptible linguistic phenomenon and therefore difficult to identify and eliminate. In this paper, we first create a novel task named implicit discourse relation disambiguation (IDRD). Second, we propose a focus-sensitive relation disambiguation model that affirms a truly-correct relation when it is triggered by focal sentence constituents. In addition, we specifically develop a topic-driven focus identification method and a relation search system (RSS) to support the relation disambiguation. Finally, we improve current relation detection systems by using the disambiguation model. Experiments on the penn discourse treebank (PDTB) show promising improvements.

How compatible are our discourse annotation frameworks? Insights from mapping RST-DT and PDTB annotations

Discourse-annotated corpora are an important resource for the community, but they are often annotated according to different frameworks. This makes joint usage of the annotations difficult, preventing researchers from searching the corpora in a unified way, or using all annotated data jointly to train computational systems. Several theoretical proposals have recently been made for mapping the relational labels of different frameworks to each other, but these proposals have so far not been validated against existing annotations. The two largest discourse relation annotated resources, the Penn Discourse Treebank and the Rhetorical Structure Theory Discourse Treebank, have however been annotated on the same texts, allowing for a direct comparison of the annotation layers. We propose a method for automatically aligning the discourse segments, and then evaluate existing mapping proposals by comparing the empirically observed against the proposed mappings. Our analysis highlights the influence of segmentation on subsequent discourse relation labelling, and shows that while agreement between frameworks is reasonable for explicit relations, agreement on implicit relations is low. We identify several sources of systematic discrepancies between the two annotation schemes and discuss consequences for future annotation and for usage of the existing resources.

Unifying dimensions in coherence relations: How various annotation frameworks are related

AbstractIn this paper, we show how three often used and seemingly different discourse annotation frameworks – Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and Segmented Discourse Representation Theory – can be related by using a set of unifying dimensions. These dimensions are taken from the Cognitive approach to Coherence Relations and combined with more fine-grained additional features from the frameworks themselves to yield a posited set of dimensions that can successfully map three frameworks. The resulting interface will allow researchers to find identical or at least closely related relations within sets of annotated corpora, even if they are annotated within different frameworks. Furthermore, we tested our unified dimension (UniDim) approach by comparing PDTB and RST annotations of identical newspaper texts and converting their original end label annotations of relations into the accompanying values per dimension. Subsequently, rates of overlap in the attributed values per dimension were analyzed. Results indicate that the proposed dimensions indeed create an interface that makes existing annotation systems “talk to each other.”

Linguistic Properties Matter for Implicit Discourse Relation Recognition: Combining Semantic Interaction, Topic Continuity and Attribution

Modern solutions for implicit discourse relation recognition largely build universal models to classify all of the different types of discourse relations. In contrast to such learning models, we build our model from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank (PDTB) styled implicit discourse relations: Comparison, Contingency and Expansion. We find semantic characteristics of each relation type and two cohesion devices---topic continuity and attribution---work together to contribute such linguistic properties. We encode those properties as complex features and feed them into a NaiveBayes classifier, bettering baselines(including deep neural network ones) to achieve a new state-of-the-art performance level. Over a strong, feature-based baseline, our system outperforms one-versus-other binary classification by 4.83% for Comparison relation, 3.94% for Contingency and 2.22% for four-way classification.

Quantitative Aspects of PDTB-Style Discourse Relations across Languages

Frequency distribution of words, syntax and semantics in many languages abides by certain laws. However, because of the shortage of discourse corpora, few studies have examined whether the frequency of discourse relations follows some distributional patterns. Although there is some research based on the Rhetorical Structure Theory discourse treebank (RST-DT), each of these studies is limited to a single language. Otherwise to the RST-DT, the Penn Discourse Treebank (PDTB), adopting another annotation system, has had an enormous influence on the study of discourse structure and discourse annotation. Discourse corpora in other languages, such as Chinese, Hindi, Turkish, Czech and Arabic have been annotated following PDTB style. With the data from these discourse treebanks, we find that the rank-frequency of discourse relations follow the same pattern and that these languages share significant similarities in using semantic relations to organize the discourse. It is evidenced in our research that humans assume the relationship between two consecutive sentences is a causal connection or expansion link for fewer connectives used, but the relation of contrast is the most marked by connectives. This research will be of significance for understanding the homogeneity of discourse structure across languages.

Signalling Implicit Relations: A PDTB - RST Comparison

Describing implicit phenomena in discourse is known to be a problematic task, from both theoretical and empirical perspectives. The present article contributes to this topic by a novel comparative analysis of two prominent annotation approaches to discourse relations (coherence relations) that were carried out on the same texts. We compare the annotation of implicit relations in the Penn Discourse Treebank 2.0, i.e. discourse relations not signaled by an explicit discourse connective, to the recently released analysis of signals of rhetorical relations in the RST Signalling Corpus (RST-SC). The intersection of corresponding pairs of relations is rather a small one, but it shows a clear tendency: unlike the overall signal distribution in the RST-SC, more than half of the signals in the studied intersection are of semantic type, formed mostly by loosely defined lexical chains. Our data transformation allows for a simultaneous depiction and detailed study of the two resources.

A CDT-Styled End-to-End Chinese Discourse Parser

Discourse parsing is a challenging task and plays a critical role in discourse analysis. Since the release of the Rhetorical Structure Theory Discourse Treebank and the Penn Discourse Treebank, the research on English discourse parsing has attracted increasing attention and achieved considerable success in recent years. At the same time, some preliminary research on certain subtasks about discourse parsing for other languages, such as Chinese, has been conducted. In this article, we present an end-to-end Chinese discourse parser with the Connective-Driven Dependency Tree scheme, which consists of multiple components in a pipeline architecture, such as the elementary discourse unit (EDU) detector, discourse relation recognizer, discourse parse tree generator, and attribution labeler. In particular, the attribution labeler determines two attributions (i.e., sense and centering) for every nonterminal node (i.e., discourse relation) in the discourse parse trees. Systematically, our parser detects all EDUs in a free text, generates the discourse parse tree in a bottom-up way, and determines the sense and centering attributions for all nonterminal nodes by traversing the discourse parse tree. Comprehensive evaluation on the Connective-Driven Dependency Treebank corpus from both component-wise and error-cascading perspectives is conducted to illustrate how each component performs in isolation, and how the pipeline performs with error propagation. Finally, it shows that our end-to-end Chinese discourse parser achieves an overall F1 score of 20% with full automation.

Using Discourse Signals for Robust Instructor Intervention Prediction

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses.

A Psycholinguistic Model for the Marking of Discourse Relations

Discourse relations can either be explicitly marked by discourse connectives (DCs), such as therefore and but, or implicitly conveyed in natural language utterances. How speakers choose between the two options is a question that is not well understood. In this study, we propose a psycholinguistic model that predicts whether or not speakers will produce an explicit marker given the discourse relation they wish to express. Our model is based on two information-theoretic frameworks: (1) the Rational Speech Acts model, which models the pragmatic interaction between language production and interpretation by Bayesian inference, and (2) the Uniform Information Density theory, which advocates that speakers adjust linguistic redundancy to maintain a uniform rate of information transmission. Specifically, our model quantifies the utility of using or omitting a DC based on the expected surprisal of comprehension, cost of production, and availability of other signals in the rest of the utterance. Experiments based on the Penn Discourse Treebank show that our approach outperforms the state-of-the-art performance at predicting the presence of DCs (Patterson and Kehler, 2013), in addition to giving an explanatory account of the speaker’s choice.

Annotations of Connectives and Arguments in Malayalam Language

Discourse relations in natural languages link clauses in text and compose overall text structure. Discourse connectives are an important part of modeling the Malayalam discourse structure. We followed the annotation procedure of Penn Discourse Tree Bank and worked on tagging of discourse connectives and arguments of Malayalam text and also report the senses of relation. We present our work on annotations of Malayalam discourse connectives and arguments which helps to know more about the discourse connectives and their appearance in case of semantic rules in Malayalam discourse. Discourse connectives may or may not be explicitly present in the relation. In our work, we focus on the annotation of both explicit and implicit connectives and arguments in Malayalam text and showed encouraging results.