Papers by Etsuko Ishii
VScript: Controllable Script Generation with Visual Presentation (2022.aacl-demo)
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Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
| Challenge: | Using a script generation system, scriptwriters can customize their scripts using video retrieval. |
| Approach: | They propose a controllable pipeline that generates complete scripts, including dialogues and scene descriptions, and presents visually using video retrieval. |
| Outcome: | The proposed system outperforms baselines on both automatic and human evaluations, especially in genre control. |
What Makes for Good Image Captions? (2025.findings-emnlp)
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| Challenge: | a formal information-theoretic framework is developed for image captioning . the pyramid of captions is a method that generates enriched captions by integrating local and global visual information. |
| Approach: | They propose a formal information-theoretic framework for image captioning . they propose 'Pyramid of Captions' method that generates enriched captions . |
| Outcome: | The proposed framework provides a flexible foundation for analyzing and optimizing image captioning systems across diverse task requirements. |
Supplement Generation Training for Enhancing Agentic Task Performance (2026.findings-acl)
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Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli, Salvatore Romeo, Dongwei Jiang, Khushbu Pahwa, Yubin Ge, Etsuko Ishii, Monica Sunkara, Yi Zhang
| Challenge: | Training large foundation models for agentic tasks is impractical due to high computational costs, long iteration cycles, and rapid obsolescence as new models are released. |
| Approach: | They propose a method that trains a small LLM to generate supplemental text that helps the larger LLM solve the task more effectively. |
| Outcome: | The proposed approach decouples task-specific optimization from large foundation models . it achieves consistent and significant performance gains across diverse tasks and models - all without gradient access to the actor model. |
Towards Mitigating LLM Hallucination via Self Reflection (2023.findings-emnlp)
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| Challenge: | Large language models have shown promise for generative and knowledge-intensive tasks including question-answering (QA) but the practical deployment still faces challenges, notably the issue of “hallucination”, where models generate plausible-sounding but unfaithful or nonsensical information. |
| Approach: | They propose a self-reflection methodology that incorporates knowledge acquisition and answer generation to address the issue of "hallucination" they use a set of LLMs to generate a more accurate and factually accurate answer. |
| Outcome: | The proposed approach improves factuality, consistency, and entailment of the generated answers. |
Belief Revision: The Adaptability of Large Language Models Reasoning (2024.emnlp-main)
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| Challenge: | Existing evaluations assume language models operate with consistent information. |
| Approach: | They propose a dataset to test LMs' belief revision ability when presented with new evidence. |
| Outcome: | The proposed framework improves language models’ adaptiveness to changing information, highlighting a critical trade-off. |
High-Dimension Human Value Representation in Large Language Models (2025.naacl-long)
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Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung
| Challenge: | Existing approaches to align large language models with human values and preferences are not able to be applied to all tasks and fields. |
| Approach: | They propose a high-dimensional representation of symbolic human value distributions in LLMs that is orthogonal to model architecture and training data. |
| Outcome: | The proposed representations are evaluated on 15 open-source and commercial LLMs and are self-supervised from the value-relevant output of 8 LLM models. |
Plug-and-Play Conversational Models (2020.findings-emnlp)
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| Challenge: | Large conversational models that generate coherent and fluent responses often require large dialogue datasets. |
| Approach: | They propose and evaluate plug-and-play methods for controllable response generation . they demonstrate a high degree of control over the generated conversational responses . |
| Outcome: | The proposed method does not require further computation at decoding time and does not need fine-tuning of a large language model. |
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. |
Explicit Trait Inference for Multi-Agent Coordination (2026.acl-long)
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| Challenge: | Large language model (LLM) based multi-agent systems (MAS) show promise on complex tasks but remain prone to failures of coordination, such as goal drift, error cascades, and misaligned behaviors. |
| Approach: | They propose a psychologically grounded method for improving coordination using Explicit Trait Inference (ETI) ETI enables agents to infer and track partner characteristics along two established psychological dimensions—warmth (e.g., trust) and competence (eg. skill) |
| Outcome: | The proposed method reduces payoff loss in controlled and realistic multi-agent settings by 45–77% and improves performance by 3–29% depending on scenario and model. |
Contrastive Learning for Inference in Dialogue (2023.emnlp-main)
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| Challenge: | Recent large language models show remarkable advances in inference tasks, but their performance in inductive reasoning is far behind deductive reasoning. |
| Approach: | They propose to use negative samples to analyze inferences based on the semantic information gap between dialogue contexts and desired inference. |
| Outcome: | The proposed model improves inference generation by feeding negative samples to the models. |
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach (2022.acl-srw)
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| Challenge: | Existing approaches to improve QR performance dependencies among dialogue history dependencies are limited. |
| Approach: | They propose a reinforcement learning approach that integrates QR and CQA tasks without corresponding labeled QR datasets. |
| Outcome: | The proposed approach improves existing pipeline approaches in conversational question answering (QA) existing methods depend on assumption of corresponding QR datasets for every CQA dataset, resulting in poor performance. |