Papers with Penn Discourse Treebank
Non-Topical Coherence in Social Talk: A Call for Dialogue Model Enrichment (2020.acl-srw)
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| Challenge: | Current models of dialogue focus on utterances within a topically coherent discourse segment, not on social conversations . a pilot annotation study of NTUs is a first step towards a model capable of rationalizing conversational coherence in social talk. |
| Approach: | They conduct a pilot annotation study of social dialogues as a first step towards a Bayesian game-theoretic model . they first annotate content-based coherence relations that are not available in Disco-SPICE . |
| Outcome: | The proposed model can rationalize conversational coherence in social talk, the authors say . the study focuses on the natural occurring social dialogues in the Disco-SPICE corpus . |
CITE: A Corpus of Image-Text Discourse Relations (N19-1)
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| Challenge: | a crowd-sourced resource characterizes inferences in image-text contexts in the domain of cooking recipes . a recent study has found that image-image presentations are more effective at integrating text and image . |
| Approach: | They propose a crowd-sourced resource for multimodal discourse characterizing inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. |
| Outcome: | The proposed corpus enables a better understanding of communication and common-sense reasoning . it is particularly important for automating the understanding and generation of text-image presentations . |
The Role of Context and Uncertainty in Shallow Discourse Parsing (2022.coling-1)
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| Challenge: | Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning. |
| Approach: | They hypothesize that context plays an important role in accurate human annotation and add uncertainty measures can improve model accuracy and calibration. |
| Outcome: | The proposed model can be better calibrated by adding uncertainty measures to models with better accuracy and calibration. |
Towards Identifying Alternative-Lexicalization Signals of Discourse Relations (2022.coling-1)
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| Challenge: | Existing shallow discourse parsing methods have been limited to identifying relations signaled by a discourse connective and those without a signal. |
| Approach: | They propose to identify relations signalled by a discourse connective and those without . they compare a pattern-based approach and a sequence labeling model . |
| Outcome: | The proposed approach is based on a pattern-based approach and a sequence labeling model. |
Announcing the Prague Discourse Treebank 3.0 (2024.lrec-main)
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| Challenge: | PDiT 3.0 contains 21,662 discourse relations (plus 445 list relations) in 49 thousand sentences. |
| Approach: | They present the Prague Discourse Treebank 3.0, a new version of the annotation of discourse relations marked by primary and secondary discourse connectives in the Prague Dependency Treebank. |
| Outcome: | The new version of the PDiT 3.0 brings a largely revised annotation of discourse relations and achieves consistency with a Lexicon of Czech Discourse Connectives (CzeDLex) and sense taxonomy. |
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain (2021.acl-short)
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| Challenge: | Discourse relation classification is a challenging task when the text domain is different from the standard Penn Discourse Treebank (PDTB) training corpus domain. |
| Approach: | They propose to use the Biomedical Discourse Relation Bank to improve discourse relational argument representation by linking explicit instances of similar relations with a voting pipeline. |
| Outcome: | The proposed model outperforms the pre-trained BioBERT model by 2% points. |
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks (2020.emnlp-main)
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| Challenge: | TED-CDB dataset is a unique corpus of spoken discourse in Chinese . TED is based on the concept that discourse relations are grounded in an identifiable set of discourse connectives or Altlex expressions. |
| Approach: | They have created a dataset that annotates TED talks in Chinese . they propose to adapt the dataset to Chinese news text to improve its performance . |
| Outcome: | The TED-CDB dataset can improve the performance of systems for languages other than Chinese . it is adapted to features that are not present in English and can extract discourse semantic features . |
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News (2022.lrec-1)
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Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli, Nelleke Oostdijk, Tadashi Nomoto, Hansi Hettiarachchi, Iqra Ameer, Onur Uca, Farhana Ferdousi Liza, Tiancheng Hu
| Challenge: | Existing annotation guidelines for event causality focus on only explicit relations or clauses. |
| Approach: | They propose an annotation schema for event causality that addresses these concerns . they annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. |
| Outcome: | The proposed annotation schema for event causality addresses these concerns . it performs well with 81.20% F1 score on test set and 83.46% in 5-folds cross-validation . |
Inducing Discourse Marker Inventories from Lexical Knowledge Graphs (2022.lrec-1)
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| Challenge: | Discourse marker inventories are important tools for the development of discourse parsers and corpora with discourse annotations. |
| Approach: | They explore the potential of multilingual lexical knowledge graphs to induce multilingual discourse marker lexicons using concept propagation methods previously developed in translation inference across dictionaries. |
| Outcome: | The proposed method can induce multilingual discourse marker lexicons using multilingual knowledge graphs. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
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| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
Cost-Effective Discourse Annotation in the Prague Czech–English Dependency Treebank (2024.lrec-main)
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| Challenge: | a method for obtaining a high-quality annotation of explicit discourse relations is a resource-demanding task. |
| Approach: | They propose a method for obtaining a high-quality annotation of explicit discourse relations in the Czech part of the Prague Czech–English Dependency Treebank. |
| Outcome: | The proposed method solves the problem of identifying discrepancies between the annotations in the Czech part of the Penn Treebank. |
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings (P19-1)
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| Challenge: | Existing models for implicit discourse relation recognition lack the ability to accurately map connectives into discourse relations. |
| Approach: | They propose a multi-task learning framework where relations and connectives are simultaneously predicted and leveraged to transfer knowledge between the two prediction tasks. |
| Outcome: | The proposed framework yields state-of-the-art performance on several settings of the Penn Discourse Treebank dataset. |
DisSent: Learning Sentence Representations from Explicit Discourse Relations (P19-1)
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| Challenge: | Existing models train on vast amounts of text or require costly, manually curated datasets. |
| Approach: | They propose to leverage the discourse relations between sentences to curate a high quality sentence relation task by leveraging explicit discourse relations. |
| Outcome: | The proposed model can be used to learn the meaning of two sentences in a bidirectional LSTM sentence encoder. |
Multi-Label Classification for Implicit Discourse Relation Recognition (2024.findings-acl)
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| Challenge: | Prior research in discourse relation recognition has treated these instances as separate examples during training, with a gold-standard prediction matching one of the labels considered correct at test time. |
| Approach: | They propose to use multiple labels to annotate an example when multiple relations are believed to hold simultaneously. |
| Outcome: | The proposed frameworks don't depress performance for single-label prediction. |
Discourse Sense Flows: Modelling the Rhetorical Style of Documents across Various Domains (2023.findings-emnlp)
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| Challenge: | Recent work on shallow discourse parsing has given renewed attention to the role of discourse relation signals, in particular explicit connectives and alternative lexicalizations. |
| Approach: | They propose a model for extracting and classifying discourse relation signals from the Penn Discourse Treebank v3 corpus and introduce a new way of modeling rhetorical style by the linear order of coherence relations. |
| Outcome: | The proposed models are based on the Penn Discourse Treebank v3 corpus and employ n-gram patterns to predict genre/domain discrimination. |