Papers by Masaaki Nishino
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. |
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. |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
Robustness Evaluation of Text Classification Models Using Mathematical Optimization and Its Application to Adversarial Training (2022.findings-aacl)
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| Challenge: | Neural networks are vulnerable to adversarial examples due to slightly perturbed input data. |
| Approach: | They propose a method that evaluates the robustness of text classification models by an optimization problem that identifies a minimum synonym swap that changes the classification result. |
| Outcome: | The proposed method achieves high scores in human evaluations of grammatical correctness and semantic similarity for an IMDb dataset and implements adversarial training with the IMD and SST2 datasets. |
A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT (2020.emnlp-main)
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| Challenge: | supervised word alignment tools such as GIZA++, MGIZA (Gao and Vogel, 2008) and FastAlign remain stagnant in terms of word alignment accuracy. |
| Approach: | They propose a supervised word alignment method based on cross-language span prediction by formalizing a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. |
| Outcome: | The proposed method significantly outperforms previous supervised and unsupervised word alignment methods without any bitexts for pretraining. |