Papers by Kotaro Funakoshi
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for text infilling focus on the infill length of blanks and attribute relevance, but attribute-aware content can be more useful. |
| Approach: | They propose an attribute-aware text infilling method via a Pre-trained language model which contains a text in filling component and a plug-and-play discriminator. |
| Outcome: | The proposed method improves attribute relevance without decreasing text fluency on three open-source datasets. |
Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods modify attention mechanism to be bidirectional, undermining LLMs’ ability to extract semantic information acquired during pre-training. |
| Approach: | They propose a general-purpose embedding model that pre-encodes input text into a single Contextual token and then prepends it to the LLM's input sequence. |
| Outcome: | The proposed model improves performance of decoder-only large language models without altering their architectures or introducing significant computational overhead. |
Generative Replay Inspired by Hippocampal Memory Indexing for Continual Language Learning (2023.eacl-main)
Copied to clipboard
| Challenge: | Continual learning (CL) is a fundamental requirement for human-like general intelligence (Parisi et al., 2019). |
| Approach: | They propose to control sample generation using compressed features of previous training samples by using hippocampal memory indexing to enhance the generative replay. |
| Outcome: | The proposed method outperforms current generative replay methods and generates training samples from previous tasks. |
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation (2025.acl-long)
Copied to clipboard
| Challenge: | Maximum a posteriori decoding aims to maximize the estimated posterior probability, but high estimated probability does not always lead to high translation quality. |
| Approach: | They propose a method that seeks hypotheses with the highest expected utility by using quasi-sources as “support hypothese . they propose sMBR decoding which utilizes a reference-free quality estimation metric as the utility function. |
| Outcome: | The proposed approach outperforms QE reranking and the standard MBR decoding. |
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimoda Emotion Recognition (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing graph-based methods fail to depict global contextual features and local diverse unimodal features in a dialogue. |
| Approach: | They propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition using a multimodal fusion mechanism and a graph contrastative learning framework. |
| Outcome: | The proposed method improves multimodal emotion recognition on unbalanced and small-scale emotional datasets. |
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been widely explored for embedding generation. |
| Approach: | They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden. |
| Outcome: | The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark. |
Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
| Approach: | They propose a framework for numerical table-to-text generation based on numerical reasoning . they use a pre-trained model and a copy mechanism to fine-tune the models to produce fluent text . |
| Outcome: | The proposed framework lacks fidelity to the table contents and is based on a pre-trained model and a copy mechanism. |
myMediCon: End-to-End Burmese Automatic Speech Recognition for Medical Conversations (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing medical conversation speech corpora for Burmese are limited, despite advances in ASR. |
| Approach: | They propose to use a manually curated medical conversation speech corpus for Burmese to examine the performance of ASR models. |
| Outcome: | The proposed model outperforms the Transformer model and the Recurrent Neural Network (RNN) models. |
Generating Repetitions with Appropriate Repeated Words (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies have focused on general response generation with neural network-based approaches, but none have addressed specific types of repetitions. |
| Approach: | They propose a weighted label smoothing method for explicitly learning which words to repeat during fine-tuning and a repetition scoring method that can output more appropriate repetitions during decoding. |
| Outcome: | The proposed method outperforms baselines in automatic and human evaluations on a pre-trained language model for generating repetitions. |
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to extract word embeddings from training datasets are not efficient for training other models. |
| Approach: | They propose a method to distill a training dataset into a textual model by combining a small number of informative synthetic samples. |
| Outcome: | The proposed method outperforms existing methods on training datasets and language models. |
Emotion Recognition in Multi-Speaker Conversations through Speaker Identification, Knowledge Distillation, and Hierarchical Fusion (2026.findings-eacl)
Copied to clipboard
| Challenge: | Emotion recognition in multi-speaker conversations faces significant challenges due to speaker ambiguity and severe class imbalance. |
| Approach: | They propose a speaker identification module that leverages audio-visual synchronization to accurately identify the active speaker and hierarchical attention fusion with composite loss functions to handle class imbalance. |
| Outcome: | The proposed framework achieves 67.75% and 72.44% weighted F1 scores on MELD and IEMOCAP datasets, with notable improvements on minority emotion classes. |
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies on timeline summarization ignore the information interaction between sentences and dates, and combine them as two separate tasks. |
| Approach: | They propose a joint learning-based heterogeneous graph attention network for timeline summarization (HeterTls) they combine date selection and event detection into a unified framework to improve extraction accuracy . |
| Outcome: | The proposed model outperforms state-of-the-art models on four datasets . it significantly outperformed the baseline models on ROUGE scores and date selection metrics . |
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-modal machine translation methods are underperforming compared to pre-trained models due to lack of triplet training data. |
| Approach: | They propose a multi-modal machine translation method that integrates images and visual modality to enhance language understanding. |
| Outcome: | The proposed method can enrich the original samples and expand the dataset without requiring external images and text. |
Dataset Distillation with Attention Labels for Fine-tuning BERT (2023.acl-short)
Copied to clipboard
| 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. |
Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification (2024.eacl-short)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pre-trained models require massive computational resources and time. |
| Approach: | They propose a novel approach for fine-tuning a pre-trained model using backpropagation and an iterative extreme learning machine for training a classifier. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in training-time measurement and performance with comparable model performance. |
Live Football Commentary System Providing Background Information (2025.acl-demo)
Copied to clipboard
Yuichiro Mori, Chikara Tanaka, Aru Maekawa, Satoshi Kosugi, Tatsuya Ishigaki, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | Existing studies on sports commentary generation focus on describing major events in the video, but real-world commentary often includes background information. |
| Approach: | They developed an audio commentary system that generates utterances with background information and play-by-play commentary for football matches. |
| Outcome: | The proposed system generates utterances with background information and play-by-play commentary for football matches. |
Generating Weather Comments from Meteorological Simulations (2021.eacl-main)
Copied to clipboard
Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | generating weather-forecast comments from meteorological simulations is labor intensive and requires a solid knowledge of meteorology. |
| Approach: | They propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. |
| Outcome: | The proposed model performs best against baselines in terms of informativeness . it is available online and the results are available to the general public . |