Author name cluster

Gaku Morio

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9 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

A Multimodal Benchmark for Framing of Oil & Gas Advertising and Potential Greenwashing Detection

Gaku Morio
Harri Rowlands
Dominik Stammbach
Christopher D Manning
Peter Henderson

Companies spend large amounts of money on public relations campaigns to project a positive brand image. However, sometimes there is a mismatch between what they say and what they do. Oil & gas companies, for example, are accused of "greenwashing" with imagery of climate-friendly initiatives. Understanding the framing, and changes in framing, at scale can help better understand the goals and nature of public relation campaigns. To address this, we introduce a benchmark dataset of expert-annotated video ads obtained from Facebook and YouTube. The dataset provides annotations for 13 framing types for more than 50 companies or advocacy groups across 20 countries. Our dataset is especially designed for the evaluation of vision-language models (VLMs), distinguishing it from past text-only framing datasets. Baseline experiments show some promising results, while leaving room for improvement for future work: GPT-4. 1 can detect environmental messages with 79% F1 score, while our best model only achieves 46% F1 score on identifying framing around green innovation. We also identify challenges that VLMs must address, such as implicit framing, handling videos of various lengths, or implicit cultural backgrounds. Our dataset contributes to research in multimodal analysis of strategic communication in the energy sector.

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AAAI Conference 2024 System Paper

CHICOT: A Developer-Assistance Toolkit for Code Search with High-Level Contextual Information

Terufumi Morishita
Yuta Koreeda
Atsuki Yamaguchi
Gaku Morio
Osamu Imaichi
Yasuhiro Sogawa

We propose a source code search system named CHICOT (Code search with HIgh level COnText) to assist developers in reusing existing code. While previous studies have examined code search on the basis of code-level, fine-grained specifications such as functionality, logic, or implementation, CHICOT addresses a unique mission: code search with high-level contextual information, such as the purpose or domain of a developer's project. It achieves this feature by first extracting the context information from codebases and then considering this context during the search. It provides a VSCode plugin for daily coding assistance, and the built-in crawler ensures up-to-date code suggestions. The case study attests to the utility of CHICOT in real-world scenarios.

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NeurIPS Conference 2024 Conference Paper

Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus

Terufumi Morishita
Gaku Morio
Atsuki Yamaguchi
Yasuhiro Sogawa

Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (ALT)}$, which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples. We first establish principles for designing high-quality samples by integrating symbolic logic theory and previous empirical insights. Then, based on these principles, we construct a synthetic corpus named $\textbf{Formal} \ \textbf{Logic} \ \textbf{\textit{D}eduction} \ \textbf{\textit{D}iverse}$ (FLD$ _{\times2}$), comprising numerous samples of multi-step deduction with unknown facts, diverse reasoning rules, diverse linguistic expressions, and challenging distractors. Finally, we empirically show that ALT on FLD$ _{\times2}$ substantially enhances the reasoning capabilities of state-of-the-art LLMs, including LLaMA-3. 1-70B. Improvements include gains of up to 30 points on logical reasoning benchmarks, up to 10 points on math and coding benchmarks, and 5 points on the benchmark suite BBH.

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IJCAI Conference 2024 Conference Paper

ReportParse: A Unified NLP Tool for Extracting Document Structure and Semantics of Corporate Sustainability Reporting

Gaku Morio
Soh Young In
Jungah Yoon
Harri Rowlands
Christopher Manning

We introduce ReportParse, a Python-based tool designed to parse corporate sustainability reports. It combines document structure analysis with natural language processing (NLP) models to extract sustainability-related information from the reports. We also provide easy-to-use web and command interfaces. The tool is expected to aid researchers and analysts in evaluating corporate commitment to and management of sustainability efforts.

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NeurIPS Conference 2023 Conference Paper

An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement

Gaku Morio
Christopher D Manning

As societal awareness of climate change grows, corporate climate policy engagements are attracting attention. We propose a dataset to estimate corporate climate policy engagement from various PDF-formatted documents. Our dataset comes from LobbyMap (a platform operated by global think tank InfluenceMap) that provides engagement categories and stances on the documents. To convert the LobbyMap data into the structured dataset, we developed a pipeline using text extraction and OCR. Our contributions are: (i) Building an NLP dataset including 10K documents on corporate climate policy engagement. (ii) Analyzing the properties and challenges of the dataset. (iii) Providing experiments for the dataset using pre-trained language models. The results show that while Longformer outperforms baselines and other pre-trained models, there is still room for significant improvement. We hope our work begins to bridge research on NLP and climate change.

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ICML Conference 2023 Conference Paper

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

Terufumi Morishita
Gaku Morio
Atsuki Yamaguchi
Yasuhiro Sogawa

We study a synthetic corpus based approach for language models (LMs) to acquire logical deductive reasoning ability. The previous studies generated deduction examples using specific sets of deduction rules. However, these rules were limited or otherwise arbitrary. This can limit the generalizability of acquired deductive reasoning ability. We rethink this and adopt a well-grounded set of deduction rules based on formal logic theory, which can derive any other deduction rules when combined in a multistep way. We empirically verify that LMs trained on the proposed corpora, which we name $\textbf{FLD}$ ($\textbf{F}$ormal $\textbf{L}$ogic $\textbf{D}$eduction), acquire more generalizable deductive reasoning ability. Furthermore, we identify the aspects of deductive reasoning ability on which deduction corpora can enhance LMs and those on which they cannot. Finally, on the basis of these results, we discuss the future directions for applying deduction corpora or other approaches for each aspect. We release the code, data, and models.

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ICML Conference 2022 Conference Paper

Rethinking Fano's Inequality in Ensemble Learning

Terufumi Morishita
Gaku Morio
Shota Horiguchi
Hiroaki Ozaki
Nobuo Nukaga

We propose a fundamental theory on ensemble learning that evaluates a given ensemble system by a well-grounded set of metrics. Previous studies used a variant of Fano’s inequality of information theory and derived a lower bound of the classification error rate on the basis of the accuracy and diversity of models. We revisit the original Fano’s inequality and argue that the studies did not take into account the information lost when multiple model predictions are combined into a final prediction. To address this issue, we generalize the previous theory to incorporate the information loss. Further, we empirically validate and demonstrate the proposed theory through extensive experiments on actual systems. The theory reveals the strengths and weaknesses of systems on each metric, which will push the theoretical understanding of ensemble learning and give us insights into designing systems.

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AAAI Conference 2021 System Paper

i-Parser: Interactive Parser Development Kit for Natural Language Processing

Gaku Morio
Hiroaki Ozaki
Yuta Koreeda
Terufumi Morishita
Toshinori Miyoshi

This demonstration paper presents i-Parser, a novel development kit that produces high-performance semantic parsers. i- Parser converts training graphs into sequences written in a context-free language, then our proposed model learns to generate the sequences. With interactive configuration and visualization, users can easily build their own parsers. Benchmark results of i-Parser showed high performances of various parsing tasks in natural language processing.

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AAAI Conference 2019 Short Paper

On the Role of Syntactic Graph Convolutions for Identifying and Classifying Argument Components

Gaku Morio
Katsuhide Fujita

This paper focuses on fundamental research that combines syntactic knowledge with neural studies, which utilize syntactic information in argument component identification and classification (AC-I/C) tasks in argument mining (AM). The following are our paper’s contributions: 1) We propose a way of incorporating a syntactic GCN into multi-task learning models for AC-I/C tasks. 2) We demonstrate the valid effectiveness of our proposed syntactic GCN in fair experiments in some datasets. We also found that syntactic GCNs are promising for lexically independent scenarios. Our code in the experiments is available for reproducibility. 1

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