Papers by Tsutomu Hirao
Pruning Basic Elements for Better Automatic Evaluation of Summaries (N18-2)
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| Challenge: | Summarization studies work on increasing the scores that are given by automatic evaluation measures. |
| Approach: | They propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements. |
| Outcome: | The proposed measure outperforms ROUGE and BE in most cases and achieves highest correlation coefficient in TAC 2011 AESOP task. |
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. |
WikiSplit++: Easy Data Refinement for Split and Rephrase (2024.lrec-main)
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| Challenge: | Existing text simplification methods rely on encoder-decoder models to achieve this task. |
| Approach: | They propose a text-to-text generation approach that applies encoder-decoder models to a large-scale dataset to improve Split and Rephrase. |
| Outcome: | The proposed approach improves Split and Rephrase readability and performance on large datasets, but still suffers from hallucinations and under-splitting. |
Automatic Evaluation of Language Generation Technology Based on Structure Alignment (2025.coling-main)
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| Challenge: | Existing methods for automatic evaluation ignore syntax of sentences despite its importance in determining meaning. |
| Approach: | They propose an automatic evaluation metric that considers both the words in sentences and their syntactic structures. |
| Outcome: | The proposed method is comparable to baselines from two NLP tasks. |
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. |
Implicit Sense-labeled Connective Recognition as Text Generation (2023.findings-emnlp)
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| Challenge: | Existing methods for identifying implicit discourse relations are limited by the number of possible categories and sense labels. |
| Approach: | They propose a method for identifying the sense label of an implicit connective between adjacent text spans by using an encoder-decoder model. |
| Outcome: | The proposed method outperforms the conventional classification-based method on a shallow discourse parsing dataset. |
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries (D18-1)
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| Challenge: | Existing methods for evaluating content are not accurate because they only confirm if the summary contains small textual fragments. |
| Approach: | They propose to transform human-made reference summaries into extractive reference sums and weight them using elementary discourse units. |
| Outcome: | The proposed method strongly correlates with manual evaluations on DUC and TAC data sets. |
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function (N18-1)
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| Challenge: | Submodular maximization with the greedy algorithm is an effective approach to extractive summarization. |
| Approach: | They propose a submodular maximization method that is 100 to 400 times faster than existing methods for extractive summarization. |
| Outcome: | The proposed method is 100 to 400 times faster than existing method based on integer-linear-programming formulations and achieves 95%-approximation. |
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts (2020.findings-emnlp)
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| Challenge: | Existing methods for dividing biomedical abstracts into rhetorical segments assign a rhetorical label to each sentence while considering context in the abstract. |
| Approach: | They propose to use Neural Semi-Markov Conditional Random Fields to assign a rhetorical label to a span that consists of continuous sentences. |
| Outcome: | The proposed method achieved the best micro sentence-F1 score and the best macro span-F1. |
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. |
Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints (D19-1)
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| Challenge: | Existing methods for creating anagrams do not pay much attention to the naturalness of the generated anagramms. |
| Approach: | They propose to combine depth-first search with modern neural language models to generate anagrams by permutation of characters in an input sentence or phrase. |
| Outcome: | The proposed method generates significantly more natural anagrams than baseline methods. |
Argument Mining as a Text-to-Text Generation Task (2024.eacl-long)
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| Challenge: | Argument Mining (AM) aims to uncover the argumentative structures within a text. |
| Approach: | They propose a method that generates argumentatively annotated text using a pretrained encoder-decoder language model and a pre-trained decoder. |
| Outcome: | The proposed method achieves state-of-the-art performance on three types of benchmark datasets. |
Higher-Order Syntactic Attention Network for Longer Sentence Compression (N18-1)
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| Challenge: | Existing sentence compression methods do not handle syntactic features, causing performance degradation . et al. (2015) reported that the longer the input sentences are, the worse the performance becomes. |
| Approach: | They propose a higher-order syntactic attention network that handles higher-level dependency features as an attention distribution on LSTM hidden states. |
| Outcome: | The proposed method outperforms baseline methods on a Google sentence compression dataset. |
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. |
Can we obtain significant success in RST discourse parsing by using Large Language Models? (2024.eacl-long)
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| Challenge: | Experimental results show that LLMs with tens of billion parameters can perform discourse parsing tasks. |
| Approach: | They employ Llama 2 and fine-tune it with QLoRA to achieve similar results . they show that LLMs with tens of billion parameters can perform a wide range of NLP tasks . |
| Outcome: | The proposed model performs better than existing models on three benchmark datasets. |
Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs (2024.findings-acl)
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| Challenge: | Neural machine translation (NMT) systems do not take into account the complexity of the words used to compose the translations. |
| Approach: | They propose a method that replaces high Age of Acquisitions words in translations with simpler words to match the user’s level. |
| Outcome: | The proposed method replaces high-AoA words with lower-Aa words while maintaining high BLEU and COMET scores. |