Papers by Issei Yoshida
A Simple Yet Effective Hybrid Pre-trained Language Model for Unsupervised Sentence Acceptability Prediction (2022.aacl-short)
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| Challenge: | Existing unsupervised prediction approaches rely on language models to estimate sentence acceptability . low-frequency words would have a significant negative impact on sentence likelihood . |
| Approach: | They propose a method that substitutes Part-of-Speech (POS) tags for low-frequency words in sentences . their method improves both a sentence acceptability benchmark and a cross-domain sentence evaluation corpus . |
| Outcome: | The proposed method improves on a sentence acceptability benchmark and a cross-domain sentence evaluation corpus. |
Interactive Construction of User-Centric Dictionary for Text Analytics (2020.acl-main)
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| Challenge: | Existing methods for interactive dictionary construction are limited to a small number of terms, but we propose a method that can be used to create flexible dictionaries with precise granularity. |
| Approach: | They propose a method to construct a term dictionary for text analytics through an interactive process between a human and a machine. |
| Outcome: | The proposed method outperforms baseline methods and works even with a small number of interactions. |
A Simple-Yet-Efficient Instruction Augmentation Method for Zero-Shot Sentiment Classification (2025.coling-main)
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| Challenge: | Existing studies have used labeled sentiment instances to instruction tune LLMs, improving zero-shot sentiment classification performance. |
| Approach: | They propose a simple-yet-efficient method which does not rely on actual labeled sentiment instances. |
| Outcome: | The proposed method outperforms LLMs tuned with more complex instruction tuning methods by 5.1 points and increases scores by 30 points. |
A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression (2022.lrec-1)
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| Challenge: | Deletion-based sentence compression has made significant progress in the english language . however, there is a lack of large-scale and high-quality parallel corpus for the Chinese language to train an efficient system. |
| Approach: | They propose to construct a Chinese corpus with 151k pairs of sentences and train extractive and generative neural compression models on the constructed corpus. |
| Outcome: | The proposed method generates high-quality compressed sentences on automatic and human evaluation metrics compared with baselines. |
Sentence Identification with BOS and EOS Label Combinations (2023.findings-eacl)
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| Challenge: | Existing methods for preprocessing sentences only use the end of the sentence (EOS) however, real-world texts often contain non-sentential units (NSUs) such as metadata, sentence fragments, etc. |
| Approach: | They propose a task of sentence identification where the goal is to identify SUs while excluding NSUs in a given text. |
| Outcome: | The proposed method outperforms baselines which only use EOS labels on the sentence identification task. |
Incorporating Syntactic Knowledge into Pre-trained Language Model using Optimization for Overcoming Catastrophic Forgetting (2023.findings-emnlp)
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| Challenge: | Pre-trained language models lack syntactic knowledge for many tasks that handle complex or long sentences. |
| Approach: | They propose to use pre-trained language models to incorporate syntactic knowledge into a model by adding additional syntatic knowledge to the model. |
| Outcome: | The proposed model can be easily applied to downstream tasks that require syntactic knowledge. |