Search Results for author: Naoya Inoue

Found 36 papers, 12 papers with code

Exploring Methodologies for Collecting High-Quality Implicit Reasoning in Arguments

1 code implementation EMNLP (ArgMining) 2021 Keshav Singh, Farjana Sultana Mim, Naoya Inoue, Shoichi Naito, Kentaro Inui

Annotation of implicit reasoning (i. e., warrant) in arguments is a critical resource to train models in gaining deeper understanding and correct interpretation of arguments.

Vocal Bursts Intensity Prediction

IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments

1 code implementation LREC 2022 Keshav Singh, Naoya Inoue, Farjana Sultana Mim, Shoichi Naito, Kentaro Inui

To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines.

NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning

no code implementations8 Feb 2024 Yufeng Zhao, Yoshihiro Sakai, Naoya Inoue

In-Context Learning (ICL) is suffering from unsatisfactory performance and under-calibration due to high prior bias and unfaithful confidence.

In-Context Learning

Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation

no code implementations28 Jul 2023 Camélia Guerraoui, Paul Reisert, Naoya Inoue, Farjana Sultana Mim, Shoichi Naito, Jungmin Choi, Irfan Robbani, Wenzhi Wang, Kentaro Inui

The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process.

Arukikata Travelogue Dataset

no code implementations19 May 2023 Hiroki Ouchi, Hiroyuki Shindo, Shoko Wakamiya, Yuki Matsuda, Naoya Inoue, Shohei Higashiyama, Satoshi Nakamura, Taro Watanabe

We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research.

LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments

no code implementations LREC 2022 Farjana Sultana Mim, Naoya Inoue, Shoichi Naito, Keshav Singh, Kentaro Inui

Attacking is not always straightforward and often comprise complex rhetorical moves such that arguers might agree with a logic of an argument while attacking another logic.

TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation

1 code implementation LREC 2022 Shoichi Naito, Shintaro Sawada, Chihiro Nakagawa, Naoya Inoue, Kenshi Yamaguchi, Iori Shimizu, Farjana Sultana Mim, Keshav Singh, Kentaro Inui

In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility of creating a template set that satisfies these criteria as a first trial.

Informativeness slot-filling +1

Annotating Implicit Reasoning in Arguments with Causal Links

no code implementations26 Oct 2021 Keshav Singh, Naoya Inoue, Farjana Sultana Mim, Shoichi Naitoh, Kentaro Inui

Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge.

Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts

1 code implementation Findings (EMNLP) 2021 Allen Kim, Charuta Pethe, Naoya Inoue, Steve Skiena

We present methods to handle these errors, evaluated on a collection of 19, 347 texts from the Project Gutenberg dataset and 96, 635 texts from the HathiTrust Library.

Optical Character Recognition Optical Character Recognition (OCR)

Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension

1 code implementation EMNLP 2021 Naoya Inoue, Harsh Trivedi, Steven Sinha, Niranjan Balasubramanian, Kentaro Inui

Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system.

2k Multi-Hop Reading Comprehension

A Comparative Study on Collecting High-Quality Implicit Reasonings at a Large-scale

no code implementations16 Apr 2021 Keshav Singh, Paul Reisert, Naoya Inoue, Kentaro Inui

We construct a preliminary dataset of 6, 000 warrants annotated over 600 arguments for 3 debatable topics.

Natural Language Understanding

Two Training Strategies for Improving Relation Extraction over Universal Graph

1 code implementation EACL 2021 Qin Dai, Naoya Inoue, Ryo Takahashi, Kentaro Inui

This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale text collection.

Relation Relation Extraction +1

Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring

no code implementations13 Oct 2020 Farjana Sultana Mim, Naoya Inoue, Paul Reisert, Hiroki Ouchi, Kentaro Inui

Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.

Automated Essay Scoring Language Modelling +4

Inject Rubrics into Short Answer Grading System

no code implementations WS 2019 Tianqi Wang, Naoya Inoue, Hiroki Ouchi, Tomoya Mizumoto, Kentaro Inui

Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance.

Improving Evidence Detection by Leveraging Warrants

no code implementations WS 2019 Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, Kentaro Inui

Recognizing the implicit link between a claim and a piece of evidence (i. e. warrant) is the key to improving the performance of evidence detection.

R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason

no code implementations ACL 2020 Naoya Inoue, Pontus Stenetorp, Kentaro Inui

Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets.

Multi-Hop Reading Comprehension

Riposte! A Large Corpus of Counter-Arguments

no code implementations8 Oct 2019 Paul Reisert, Benjamin Heinzerling, Naoya Inoue, Shun Kiyono, Kentaro Inui

Counter-arguments (CAs), one form of constructive feedback, have been proven to be useful for critical thinking skills.

An Empirical Study of Span Representations in Argumentation Structure Parsing

no code implementations ACL 2019 Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui

For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.

Annotating with Pros and Cons of Technologies in Computer Science Papers

1 code implementation WS 2019 Hono Shirai, Naoya Inoue, Jun Suzuki, Kentaro Inui

Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study.

Sentiment Analysis

Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates

1 code implementation WS 2018 Paul Reisert, Naoya Inoue, Tatsuki Kuribayashi, Kentaro Inui

Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e. g. attack-support relations, stance, explicit premise/claim).

Argument Mining Document Summarization +2

A Corpus of Deep Argumentative Structures as an Explanation to Argumentative Relations

no code implementations7 Dec 2017 Paul Reisert, Naoya Inoue, Naoaki Okazaki, Kentaro Inui

Our coverage result of 74. 6% indicates that argumentative relations can reasonably be explained by our small pattern set.

An RNN-based Binary Classifier for the Story Cloze Test

no code implementations WS 2017 Melissa Roemmele, Sosuke Kobayashi, Naoya Inoue, Andrew Gordon

In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct.

Cloze Test Sentence +1

Modeling Context-sensitive Selectional Preference with Distributed Representations

no code implementations COLING 2016 Naoya Inoue, Yuichiroh Matsubayashi, Masayuki Ono, Naoaki Okazaki, Kentaro Inui

This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP).

Semantic Role Labeling

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