Papers by Masayasu Muraoka
Robust ASR Error Correction with Conservative Data Filtering (2024.emnlp-industry)
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| Challenge: | Error correction (EC) based on large language models is an emerging technology to enhance the performance of automatic speech recognition systems. |
| Approach: | They propose to pair large set of ASR hypotheses with gold references to improve linguistic acceptability over sources and be inferable from available context. |
| Outcome: | The proposed approach significantly reduces overcorrection and improves quality in out-of-domain (OOD) settings. |
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. |
Image Position Prediction in Multimodal Documents (2020.lrec-1)
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| Challenge: | Existing multimodal tasks allow machines to understand images by describing or being asked in natural language. |
| Approach: | They propose a task that predicts the positions of images in a given document . they use a dataset of 66K multimodal documents with 320K images from Wikipedia . |
| Outcome: | The proposed task outperforms baselines while the performance is far from human. |
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. |
INDUS: Effective and Efficient Language Models for Scientific Applications (2024.emnlp-industry)
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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Lima, Panagiotis Vagenas, S. Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar Lee
| Challenge: | Large language models trained on general domain corpora showed remarkable results on natural language processing tasks. |
| Approach: | They develop a suite of large language models trained on general domain corpora that address NLP tasks and smaller versions of them created using knowledge distillation. |
| Outcome: | The proposed models outperform general-purpose and domain-specific encoders on new and existing tasks and in industrial settings. |
Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment (2020.findings-emnlp)
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| Challenge: | Existing word representation methods for lexical entailment have been shown to hold on text, but they have not been tested on visual objects. |
| Approach: | They propose a word representation method derived from visual objects in associated images to tackle the lexical entailment task. |
| Outcome: | The proposed method outperforms existing unsupervised representation methods. |
A Simple Yet Strong Domain-Agnostic De-bias Method for Zero-Shot Sentiment Classification (2023.findings-acl)
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| Challenge: | a recent study shows that large language models are biased to their pre-training data, leading to poor performance in prompt templates. |
| Approach: | They propose a domain-agnostic data construction method to de-bias a given prompt template . they show that domain-based generic responses are superior to in-domain ground-truth data . |
| Outcome: | The proposed method improves sentiment analysis tasks across domains and domains . it also yields better performance than existing in-domain models . |