The Penn Discourse Treebank

By Eleni Miltsakaki, Rashmi Prasad, Aravind K. Joshi, Bonnie Lynn Webber. 2004

424 citations recorded by the source corpus.

This paper describes a new discourse-level annotation project – the Penn Discourse Treebank (PDTB) – that aims to produce a large-scale corpus in which discourse connectives are annotated, along with their arguments, thus exposing a clearly defined level of discourse structure. The PDTB is being built directly on top of the Penn Treebank and Propbank, thus supporting the extraction of useful syntactic and semantic features and providing a richer substrate for the development and evaluation of practical algorithms. We present a preliminary analysis of inter-annotator agreement – both the level of agreement and the types of inter-annotator variation.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning (2017, background): “Conjunction Prediction Finally, information about the coherence relation between two sentences is sometimes apparent in the text (Miltsakaki et al., 2004): this is the case whenever the second sentence starts with a conjunction phrase.”
QED: A Framework and Dataset for Explanations in Question Answering (2020, background): “…keeping with its modularity, we can extend QED to account for these by looking to semantic relations beyond referential equality and predicate entailment, such as set-membership noun phrase (Hearst, 1992) and interclausal (Miltsakaki et al., 2004; Lamm et al., 2018; Tandon et al., 2019) relations.”
CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures (2016, methodology): “The Penn Discourse Tree Bank (PDTB) corpus (Prasad et al., 2008) addresses the annotation of causal relations, annotating semantic rela-”
FactBank: a corpus annotated with event factuality (2009, methodology): “2005), the Penn Discourse TreeBank (Miltsakaki et al. 2004), TimeBank (Pustejovsky et al.”
A PDTB-styled end-to-end discourse parser (2012, background): “Miltsakaki et al. (2004) reported human agreements on both exact and partial matches to be 90.”
Representing Discourse Coherence: A Corpus-Based Study (2005, background): “2 Note that Miltsakaki et al. (2004) report results on annotating connectives but not on annotating whole discourse structures.”
Recent advances in deep learning based dialogue systems: a systematic survey (2021, methodology): “Open-domain Dialogue Prasad et al. (2008) Luna Dialogue dataset with Italian rela-tion annotations.”
Discourse-Aware Neural Extractive Text Summarization (2019, methodology): “Other alternatives includes Graph Bank (Wolf and Gibson, 2005) and PDTB (Miltsakaki et al., 2004).”
Neural NLG for Methodius: From RST Meaning Representations to Texts (2020, background): “…have primarily tackled texts that merely describe entities, rather than comparing them, situating them in time, discussing causal or other contingency relations among them, or constructing persuasive arguments about them, where discourse relations are crucial for coherence (Prasad et al., 2008).”
The Penn Discourse TreeBank 2.0. (2008, methodology): “0 annotation manual (PDTB-Group, 2008), links to some tools for browsing and querying the corpus, and papers and slides related to the project.”
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020, background): “The common practice is either taking only the first label or the approach in Qin et al. (2017), i.”
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021, methodology): “discourse relations in DIALOGSUM, we first collect discourse connectives from Penn Discourse Treebank (Miltsakaki et al., 2004), and check whether these connectives are included in summaries in the testset.”