Search Results for author: Kentaro Inui

Found 127 papers, 42 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.

Exploring Methods for Generating Feedback Comments for Writing Learning

1 code implementation EMNLP 2021 Kazuaki Hanawa, Ryo Nagata, Kentaro Inui

To shed light on these points, we investigate a wider range of methods for generating many feedback comments in this study.

SHAPE : Shifted Absolute Position Embedding for Transformers

no code implementations EMNLP 2021 Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, Kentaro Inui

Position representation is crucial for building position-aware representations in Transformers.

Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond

no code implementations23 May 2022 Masato Mita, Keisuke Sakaguchi, Masato Hagiwara, Tomoya Mizumoto, Jun Suzuki, Kentaro Inui

Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges.

Context Limitations Make Neural Language Models More Human-Like

no code implementations23 May 2022 Tatsuki Kuribayashi, Yohei Oseki, Ana Brassard, Kentaro Inui

In this study, we demonstrate the discrepancies in context access between modern neural language models (LMs) and humans in incremental sentence processing.

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

no code implementations4 Apr 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

no code implementations18 Jan 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 to create a template set that satisfies these criteria as a first trial.

Informativeness Slot Filling

COPA-SSE: Semi-structured Explanations for Commonsense Reasoning

1 code implementation AKBC Workshop CSKB 2021 Ana Brassard, Benjamin Heinzerling, Pride Kavumba, Kentaro Inui

We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9, 747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions.

Common Sense Reasoning Knowledge Graphs

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.

Transformer-based Lexically Constrained Headline Generation

1 code implementation EMNLP 2021 Kosuke Yamada, Yuta Hitomi, Hideaki Tamori, Ryohei Sasano, Naoaki Okazaki, Kentaro Inui, Koichi Takeda

We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer.

Headline generation

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.

Multi-Hop Reading Comprehension

SHAPE: Shifted Absolute Position Embedding for Transformers

1 code implementation13 Sep 2021 Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, Kentaro Inui

Position representation is crucial for building position-aware representations in Transformers.

SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics

1 code implementation Findings (ACL) 2021 Hitomi Yanaka, Koji Mineshima, Kentaro Inui

We also find that the generalization performance to unseen combinations is better when the form of meaning representations is simpler.

Systematic Generalization

Lower Perplexity is Not Always Human-Like

1 code implementation ACL 2021 Tatsuki Kuribayashi, Yohei Oseki, Takumi Ito, Ryo Yoshida, Masayuki Asahara, Kentaro Inui

Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.

Language Modelling

Learning to Learn to be Right for the Right Reasons

no code implementations NAACL 2021 Pride Kavumba, Benjamin Heinzerling, Ana Brassard, Kentaro Inui

Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and hard test set without superficial cues.

Meta-Learning

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

Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution

1 code implementation EMNLP 2021 Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi, Hiroki Ouchi, Kentaro Inui

Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR).

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 Extraction

Exploring Transitivity in Neural NLI Models through Veridicality

1 code implementation EACL 2021 Hitomi Yanaka, Koji Mineshima, Kentaro Inui

Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear.

Natural Language Inference Natural Language Understanding

Efficient Estimation of Influence of a Training Instance

no code implementations EMNLP (sustainlp) 2020 Sosuke Kobayashi, Sho Yokoi, Jun Suzuki, Kentaro Inui

Understanding the influence of a training instance on a neural network model leads to improving interpretability.

An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution

no code implementations COLING 2020 Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono, Hiroki Ouchi, Ryo Takahashi, Kentaro Inui

This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data.

Data Augmentation Language Modelling +3

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 +3

Langsmith: An Interactive Academic Text Revision System

no code implementations EMNLP 2020 Takumi Ito, Tatsuki Kuribayashi, Masatoshi Hidaka, Jun Suzuki, Kentaro Inui

Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English.

Evaluation of Similarity-based Explanations

2 code implementations ICLR 2021 Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, Kentaro Inui

In this study, we investigated relevance metrics that can provide reasonable explanations to users.

Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction

1 code implementation ACL 2020 Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, Kentaro Inui

The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC.

Grammatical Error Correction Language Modelling

Creating Corpora for Research in Feedback Comment Generation

no code implementations LREC 2020 Ryo Nagata, Kentaro Inui, Shin{'}ichiro Ishikawa

In this paper, we report on datasets that we created for research in feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning.

Word Rotator's Distance

1 code implementation EMNLP 2020 Sho Yokoi, Ryo Takahashi, Reina Akama, Jun Suzuki, Kentaro Inui

Accordingly, we propose a method that first decouples word vectors into their norm and direction, and then computes alignment-based similarity using earth mover's distance (i. e., optimal transport cost), which we refer to as word rotator's distance.

Semantic Similarity Semantic Textual Similarity +2

Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?

1 code implementation ACL 2020 Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui

This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.

Evaluating Dialogue Generation Systems via Response Selection

1 code implementation ACL 2020 Shiki Sato, Reina Akama, Hiroki Ouchi, Jun Suzuki, Kentaro Inui

Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation.

Dialogue Generation Response Generation

Attention is Not Only a Weight: Analyzing Transformers with Vector Norms

1 code implementation EMNLP 2020 Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui

Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing.

Machine Translation Translation +1

Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets

no code implementations21 Nov 2019 Saku Sugawara, Pontus Stenetorp, Kentaro Inui, Akiko Aizawa

Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems.

Machine Reading Comprehension

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.

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.

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 Incorporating Pseudo Data into Grammatical Error Correction

1 code implementation IJCNLP 2019 Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, Kentaro Inui

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models.

Grammatical Error Correction

Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring

no code implementations WS 2019 Tomoya Mizumoto, Hiroki Ouchi, Yoriko Isobe, Paul Reisert, Ryo Nagata, Satoshi Sekine, Kentaro Inui

This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.

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.

Can neural networks understand monotonicity reasoning?

1 code implementation WS 2019 Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, Johan Bos

Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures.

Data Augmentation Natural Language Inference

The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

no code implementations SEMEVAL 2019 Kazuaki Hanawa, Shota Sasaki, Hiroki Ouchi, Jun Suzuki, Kentaro Inui

Our system achieved 80. 9{\%} accuracy on the test set for the formal run and got the 3rd place out of 42 teams.

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

Subword-based Compact Reconstruction of Word Embeddings

1 code implementation NAACL 2019 Shota Sasaki, Jun Suzuki, Kentaro Inui

The idea of subword-based word embeddings has been proposed in the literature, mainly for solving the out-of-vocabulary (OOV) word problem observed in standard word-based word embeddings.

Word Embeddings

A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation

no code implementations WS 2019 Yuta Hitomi, Yuya Taguchi, Hideaki Tamori, Ko Kikuta, Jiro Nishitoba, Naoaki Okazaki, Kentaro Inui, Manabu Okumura

However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths.

Headline generation

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

Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning Framework

no code implementations13 Oct 2018 Shun Kiyono, Jun Suzuki, Kentaro Inui

We also demonstrate that our method has the more data, better performance property with promising scalability to the amount of unlabeled data.

General Classification Text Classification

Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions

no code implementations EMNLP 2018 Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui

As well as deriving PMI from mutual information, we derive this new measure from the Hilbert--Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC).

Machine Translation Sentence Embeddings +1

What Makes Reading Comprehension Questions Easier?

1 code implementation EMNLP 2018 Saku Sugawara, Kentaro Inui, Satoshi Sekine, Akiko Aizawa

From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions.

Machine Reading Comprehension Multiple-choice

Predicting Stances from Social Media Posts using Factorization Machines

no code implementations COLING 2018 Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, Kentaro Inui

This paper presents an approach to detect the stance of a user toward a topic based on their stances toward other topics and the social media posts of the user.

Decision Making Stance Detection

Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis

no code implementations COLING 2018 Yuichiroh Matsubayashi, Kentaro Inui

Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese.

Cross-Lingual Learning-to-Rank with Shared Representations

no code implementations NAACL 2018 Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, Kentaro Inui

Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query.

Information Retrieval Learning-To-Rank +1

Unsupervised Learning of Style-sensitive Word Vectors

no code implementations ACL 2018 Reina Akama, Kento Watanabe, Sho Yokoi, Sosuke Kobayashi, Kentaro Inui

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner.

Word Embeddings

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.

Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems

no code implementations IJCNLP 2017 Hiroki Asano, Tomoya Mizumoto, Kentaro Inui

In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage.

Grammatical Error Correction Machine Translation

Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis

no code implementations IJCNLP 2017 Yuichiroh Matsubayashi, Kentaro Inui

The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions.

A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse

1 code implementation IJCNLP 2017 Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui

This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models.

Language Modelling Word Embeddings

Analyzing the Revision Logs of a Japanese Newspaper for Article Quality Assessment

no code implementations WS 2017 Hideaki Tamori, Yuta Hitomi, Naoaki Okazaki, Kentaro Inui

We address the issue of the quality of journalism and analyze daily article revision logs from a Japanese newspaper company.

Composing Distributed Representations of Relational Patterns

1 code implementation ACL 2016 Sho Takase, Naoaki Okazaki, Kentaro Inui

Learning distributed representations for relation instances is a central technique in downstream NLP applications.

General Classification Relation Classification

Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization

no code implementations ACL 2017 Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, Kentaro Inui

We present in this paper our approach for modeling inter-topic preferences of Twitter users: for example, those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade.

Stance Detection

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

Question-Answering with Logic Specific to Video Games

no code implementations LREC 2016 Corentin Dumont, Ran Tian, Kentaro Inui

We chose a popular game called {`}Minecraft{'}, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database.

Question Answering

An Attentive Neural Architecture for Fine-grained Entity Type Classification

no code implementations WS 2016 Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel

In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts.

Classification General Classification

The Mechanism of Additive Composition

no code implementations26 Nov 2015 Ran Tian, Naoaki Okazaki, Kentaro Inui

Additive composition (Foltz et al, 1998; Landauer and Dumais, 1997; Mitchell and Lapata, 2010) is a widely used method for computing meanings of phrases, which takes the average of vector representations of the constituent words.

Cannot find the paper you are looking for? You can Submit a new open access paper.