Easily Identifiable Discourse Relations

We present a corpus study of local discourse relations based on the Penn Discourse Tree Bank, a large manually annotated corpus of explicitly or implicitly realized contingency, comparison, temporal and expansion relations. We show that while there is a large degree of ambiguity in temporal explicit discourse connectives, overall discourse connectives are mostly unambiguous and allow high accuracy classification of discourse relations. We achieve 93.09% accuracy in classifying the explicit relations and 74.74% accuracy overall. In addition, we show that some pairs of relations occur together in text more often than expected by chance. This finding suggest that global sequence classification of the relations in text can lead to better results, especially for implicit relations

Anaphora and Discourse Structure

We argue in this paper that many common adverbial phrases generally taken to signal a discourse relation between syntactically connected units within discourse structure, instead work anaphorically to contribute relational meaning, with only indirect dependence on discourse structure. This allows a simpler discourse structure to provide scaffolding for compositional semantics, and reveals multiple ways in which the relational meaning conveyed by adverbial connectives can interact with that associated with discourse structure. We conclude by sketching out a lexicalised grammar for discourse that facilitates discourse interpretation as a product of compositional rules, anaphor resolution and inference.Comment: 45 pages, 17 figures. Revised resubmission to Computational Linguistic

Compensating for processing difficulty in discourse:Effect of parallelism in contrastive relations

This study aims to establish whether the processing of different connectives (e.g., and, but) and different coherence relations (addition, contrast) can be modulated by a structural feature of the connected segments—namely, parallelism. While but is mainly used to contrast two expressions, and occurs in many different relations and has been shown to come with a processing cost. We report three self-paced reading experiments in which we manipulate whether the connected segments share a common verb phrase. Such parallel constructions frequently occur in contrastive relations, although they are typically treated as additive in comprehension research. We expect that parallelism will compensate for the cognitive complexity of contrast and for the ambiguity of and by further signaling the coherence relation. Our results indicate that parallelism speeds up processing and provides further evidence for priming in comprehension. However, parallelism interacted with connective ambiguity in an overt disambiguation task (Experiment 3) but not in a more natural reading task (Experiment 2). We argue that the processing of contrast remains shallow unless disambiguation is explicitly required

Weak and strong discourse markers in speech, chat and writing:Do signals compensate for ambiguity in explicit relations?

Ambiguity in discourse is pervasive, yet mechanisms of production and processing suggest that it tends to be compensated in context. The present study sets out to analyze the combination of discourse markers (such as but or moreover) with other discourse signals (such as semantic relations or punctuation marks) across three genres (discussion, chat, and essay). The presence of discourse signals is expected to vary with the ambiguity of the discourse marker and with the genre. This analysis complements recent approaches to discourse signalling by zooming in on the different types of discourse markers with which other signals combine. The corpus annotation study uncovered three categories of marker strength—weak, intermediate, and strong—thus refining the concept of “explicitness.” Statistical modeling reveals that weak discourse markers are more often compensated than intermediate and strong markers, and that this compensation is not affected by genre variation

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

The role of non-connective discourse cues and their interaction with connectives

The disambiguation and processing of coherence relations is often investigated with a focus on explicit connectives, such as but or so. Other, non-connective cues from the context also facilitate discourse inferences, although their precise disambiguating role and interaction with connectives have been largely overlooked in the psycholinguistic literature so far. This study reports on two crowdsourcing experiments that test the role of contextual cues (parallelism, antonyms, resultative verbs) in the disambiguation of contrast and consequence relations. We compare the effect of contextual cues in conceptually different relations, and with connectives that differ in their semantic precision. Using offline tasks, our results show that contextual cues significantly help disambiguating contrast and consequence relations in the absence of connectives. However, when connectives are present in the context, the effect of cues only holds if the connective is acceptable in the target relation. Overall, our study suggests that cues are decisive on their own, but only secondary in the presence of connectives. These results call for further investigation of the complex interplay between connective types, contextual cues, relation types and other linguistic and cognitive factors