Papers with Penn Discourse Tree Bank
On the Creation of a Corpus for Coherence Evaluation of Discursive Units (2020.lrec-1)
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| Challenge: | Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering. |
| Approach: | They propose to use a variety of corruption strategies to build a corpus of incoherent pairs of sentences by swapping their discourse connective or a discourse argument. |
| Outcome: | The proposed corpus is constructed from discourse argument pairs from the Penn Discourse Tree Bank and is compared with existing corpus models. |
Multi-Relational Script Learning for Discourse Relations (P19-1)
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| Challenge: | Existing script knowledge models only represent a single event relationship, co-occurrence . this is coarse for commonsense, which should account for fine-grained relationships . |
| Approach: | They propose to view learning event embedding as a multi-relational problem . they model a rich set of event relations derived from the Penn Discourse Tree Bank . |
| Outcome: | The proposed model captures different aspects of event pairs, including cause and contrast. |
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations (2020.findings-emnlp)
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| Challenge: | Structured knowledge representations capture temporal relations between events to describe human-level representations of common scenarios. |
| Approach: | They propose to represent narrative graphs and learn contextualized event representations over them using a relational graph neural network model. |
| Outcome: | The proposed model improves performance when learning script knowledge without supervision and provides a better representation for the implicit discourse sense classification task. |
Enriching a Lexicon of Discourse Connectives with Corpus-based Data (L18-1)
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| Challenge: | Existing annotation efforts for multiple languages have focused on discourse connectives, but we have limited it to the class of connectives marking contrast and the additional relations such connectives might convey. |
| Approach: | They enrich a lexicon of italian COnnectives with real corpus data for connectives marking contrast relations in text. |
| Outcome: | The proposed resource is a valuable tool for linguistic analyses of discourse relations and the training of a classifier for NLP applications. |