Papers by Naoki Kobayashi
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing (2022.findings-emnlp)
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| Challenge: | Existing discourse parsing methods need a strong baseline for reporting reliable experimental results. |
| Approach: | They integrate existing parsing strategies with transformer-based pre-trained language models to provide a strong baseline for reporting reliable experimental results. |
| Outcome: | The proposed model outperforms the current best model using DeBERTa. |
Split or Merge: Which is Better for Unsupervised RST Parsing? (D19-1)
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| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer (2021.emnlp-main)
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| Challenge: | Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. |
| Approach: | They propose a nested tree-based extractive summarization model on RoBERTa that uses syntactic and discourse trees to represent sentences in a given document. |
| Outcome: | The proposed model outperforms baseline models on the CNN/DailyMail dataset and achieves significantly better scores than the baseline models in terms of coherence and comparable scores to the state-of-the-art models. |
Improving Neural RST Parsing Model with Silver Agreement Subtrees (2021.naacl-main)
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| Challenge: | Existing methods for Rhetorical Structure Theory (RST) parsing use supervised learning, but the RST-DT is small due to the costly annotation of RST trees. |
| Approach: | They propose to use silver data to improve RST parsing models by using annotated silver data. |
| Outcome: | The proposed method achieves the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2 . it also achieves a remarkable gain in relation score against the previous state-of-the-art parser. |
Dataset Distillation with Attention Labels for Fine-tuning BERT (2023.acl-short)
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| Challenge: | Specifically, we propose to introduce attention labels, which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities. |
| Approach: | They propose to introduce attention labels which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities. |
| Outcome: | The proposed methods perform impressively in four different NLP tasks and achieve 93.2% accuracy in AGNews, which is 98.5% of the original dataset even with only one sample per class and only one gradient step. |
Video Discourse Parsing and Its Application to Multimodal Summarization: A Dataset and Baseline Approaches (2024.findings-emnlp)
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| Challenge: | Fig. 1 shows the video's story structure and event relationships in discourse parsing. |
| Approach: | They propose to construct an RST tree for a video to represent its storyline and illustrate the event relationships between events. |
| Outcome: | The proposed model outperforms two existing approaches to video RST parsing: the ‘parsing after captioning’ framework and parser using visual features. |