Papers with Penn Discourse TreeBank
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition (2020.acl-main)
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| Challenge: | Existing approaches to implicit discourse relation recognition lack connectives as strong linguistic clues. |
| Approach: | They propose a transS-driven joint learning architecture to translate discourse relations in low-dimensional embedding space and exploit the semantic features of arguments to assist discourse understanding. |
| Outcome: | The proposed model outperforms existing systems on the Penn Discourse TreeBank. |
Shallow Discourse Annotation for Chinese TED Talks (2020.lrec-1)
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| Challenge: | Existing methods to annotate text with discourse properties are limited to newspaper articles and are not available in Chinese. |
| Approach: | They propose to annotate TED talks with Chinese-related properties using the Penn Discourse TreeBank annotation style . they propose to use planned monologues instead of written text to annnotate Chinese-specific properties. |
| Outcome: | The proposed method is able to achieve reliable results in Chinese spoken monologues, and is based on the Penn Discourse TreeBank annotation style. |
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification (2020.coling-main)
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| Challenge: | inessential words are unintentionally misjudged as attention-worthy words and assigned heavier attention weights than should be. |
| Approach: | They propose a penalty-based method to regulate the attention learning process by integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. |
| Outcome: | The proposed method improves on the Penn Discourse TreeBank corpus and is competitive compared to the state-of-the-art methods. |
Syntactic Preposing and Discourse Relations (2024.eacl-long)
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| Challenge: | Existing work on discourse has ignored non-canonical syntax . a mask-filling task shows that preposing can affect discourse-relational senses . |
| Approach: | They propose to use preposing to mark information status and structure discourse flow . they use a mask-filling task to predict when a constituent appears in canonical position . |
| Outcome: | The results show that the top-ranked mask-fillers agree more often with "gold" annotations in the Penn Discourse TreeBank than in the latter case. |
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)
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| Challenge: | Lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in literature. |
| Approach: | They propose an improved evaluation protocol for implicit relation classification on PDTB 2.0 . they report strong baseline results from pretrained sentence encoders . |
| Outcome: | The proposed evaluation protocol improves the existing framework and provides strong baseline results. |
An Assessment of Explicit Inter- and Intra-sentential Discourse Connectives in Turkish Discourse Bank (L18-1)
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| Challenge: | Discourse parsing is a challenging task for NLP. |
| Approach: | They propose to add a new set of explicit intra-sentential connectives to Turkish Discourse Bank 1.1 . they propose to evaluate the converb sense annotations and compare them to other Turkish corpus . |
| Outcome: | The proposed annotations show that the subordinators tend to select certain senses not selected by explicit inter- and intra-sentential discourse connectives in the data. |