Challenge: Existing studies on the performance of BERT for implicit discourse relation classification have not been conducted.
Approach: They propose to apply BERT to implicit discourse relation classification by performing additional pre-training on text tailored to discourse relations.
Outcome: The proposed methods outperform previous state-of-the-art models in many tasks.

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Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains (D19-1)

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Challenge: Discourse relation classification is one of the most difficult tasks in discourse parsing.
Approach: They propose a bidirectional encoder representation from transformer model that encodes a representation of likely next sentences.
Outcome: The proposed model outperforms the state-of-the-art system in 11-way classification by 8% points on the standard PDTB dataset.
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations (2021.emnlp-main)

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Challenge: Existing language models do not produce suitable representations at the discourse level.
Approach: They propose to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations by incorporating top-down connections that operate at the intermediate layers of the network.
Outcome: The proposed approach improves in 6 out of 11 tasks by detecting discourse relationship detection.
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)

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Challenge: Discourse connectives are words or phrases that signal the presence of a discourse relation.
Approach: They propose a model that generates discourse connectives between arguments and predicts discourse relations based on the generated connectives.
Outcome: The proposed model outperforms baselines on three datasets and is highly accurate.
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.
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.
What Causes the Failure of Explicit to Implicit Discourse Relation Recognition? (2024.naacl-long)

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Challenge: Prior work claimed that explicit classifiers perform poorly in implicit scenarios . a label shift occurs after connectives are removed, but no empirical evidence supports this claim .
Approach: They propose to prove that the discourse relations expressed by some explicit instances will change when connectives disappear.
Outcome: The proposed methods outperform strong baselines on PDTB 2.0, PDTT 3.0, and the GUM dataset.
Which *BERT? A Survey Organizing Contextualized Encoders (2020.emnlp-main)

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Challenge: a survey on language representation learning aims to highlight common themes . we focus on the areas of progress, compared to other fields, and discuss how each area is evaluated.
Approach: They present a survey on language representation learning to highlight common themes . they compare contributions in contextualized text encoders to ideas from other fields .
Outcome: The proposed survey aims to highlight common themes in the field of language representation learning.
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.
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.
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)

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Challenge: Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction.
Approach: They propose a paragraph-level neural network that models inter-dependencies between discourse units and discourse relation continuity and patterns and predicts a sequence of discourse relations in a sentence.
Outcome: The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB.

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