Search Results for author: Akiko Aizawa

Found 91 papers, 38 papers with code

Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap

no code implementations EMNLP (MRQA) 2021 Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question–context lexical overlap.

Data Augmentation Question Answering +3

Building Dataset for Grounding of Formulae — Annotating Coreference Relations Among Math Identifiers

1 code implementation LREC 2022 Takuto Asakura, Yusuke Miyao, Akiko Aizawa

Therefore, coreference relations between symbols need to be identified for grounding, and the task has aspects of both description alignment and coreference analysis.


Towards Grounding of Formulae

no code implementations EMNLP (sdp) 2020 Takuto Asakura, André Greiner-Petter, Akiko Aizawa, Yusuke Miyao

Our results indicate that it is worthwhile to grow the techniques for the proposed task to contribute to the further progress of mathematical language processing.

Information Retrieval Retrieval

Predicting Numerals in Natural Language Text Using a Language Model Considering the Quantitative Aspects of Numerals

no code implementations NAACL (DeeLIO) 2021 Taku Sakamoto, Akiko Aizawa

In this task, we use two evaluation metrics to evaluate the language models in terms of the symbolic and quantitative aspects of the numerals, respectively.

Common Sense Reasoning Language Modelling

A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression

no code implementations LREC 2022 Yang Zhao, Hiroshi Kanayama, Issei Yoshida, Masayasu Muraoka, Akiko Aizawa

To remedy this shortcoming, we present a dependency-tree-based method to construct a Chinese corpus with 151k pairs of sentences and compression based on Chinese language-specific characteristics.

Sentence Sentence Compression

Taxonomy of Mathematical Plagiarism

1 code implementation30 Jan 2024 Ankit Satpute, Andre Greiner-Petter, Noah Gießing, Isabel Beckenbach, Moritz Schubotz, Olaf Teschke, Akiko Aizawa, Bela Gipp

Second, we analyze the best-performing approaches to detect plagiarism and mathematical content similarity on the newly established taxonomy.

Math Question Answering +1

Solving Label Variation in Scientific Information Extraction via Multi-Task Learning

1 code implementation25 Dec 2023 Dong Pham, Xanh Ho, Quang-Thuy Ha, Akiko Aizawa

The complexity of this task is compounded by the necessity for domain-specific knowledge and the limited availability of annotated data.

Multi-Task Learning

Probing Physical Reasoning with Counter-Commonsense Context

no code implementations4 Jun 2023 Kazushi Kondo, Saku Sugawara, Akiko Aizawa

In this study, we create a CConS (Counter-commonsense Contextual Size comparison) dataset to investigate how physical commonsense affects the contextualized size comparison task; the proposed dataset consists of both contexts that fit physical commonsense and those that do not.

TEIMMA: The First Content Reuse Annotator for Text, Images, and Math

1 code implementation22 May 2023 Ankit Satpute, André Greiner-Petter, Moritz Schubotz, Norman Meuschke, Akiko Aizawa, Olaf Teschke, Bela Gipp

This demo paper presents the first tool to annotate the reuse of text, images, and mathematical formulae in a document pair -- TEIMMA.


SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation

1 code implementation15 May 2023 Junfeng Jiang, Chengzhang Dong, Sadao Kurohashi, Akiko Aizawa

In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called SuperDialseg, containing 9, 478 dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialogue-related annotations.


Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection

1 code implementation25 Apr 2023 Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde

A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.

Bias Detection

Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering

2 code implementations12 Feb 2023 Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa

To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets.

Multi-hop Question Answering Open-Ended Question Answering +1

Cross-Modal Similarity-Based Curriculum Learning for Image Captioning

no code implementations14 Dec 2022 Hongkuan Zhang, Saku Sugawara, Akiko Aizawa, Lei Zhou, Ryohei Sasano, Koichi Takeda

Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability.

Image Captioning Language Modelling

Which Shortcut Solution Do Question Answering Models Prefer to Learn?

1 code implementation29 Nov 2022 Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

We assume that the learnability of shortcuts, i. e., how easy it is to learn a shortcut, is useful to mitigate the problem.

Multiple-choice Question Answering +1

Caching and Reproducibility: Making Data Science experiments faster and FAIRer

no code implementations8 Nov 2022 Moritz Schubotz, Ankit Satpute, Andre Greiner-Petter, Akiko Aizawa, Bela Gipp

In that case, the overall effort to iteratively improve the software and rerun the experiments creates significant time pressure on the researchers.

Information Retrieval Retrieval

Debiasing Masks: A New Framework for Shortcut Mitigation in NLU

1 code implementation28 Oct 2022 Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa

We propose a new debiasing method in which we identify debiased pruning masks that can be applied to a finetuned model.

Natural Language Understanding

Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering

no code implementations26 Oct 2022 Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Specifically, we find that when the relative positions in a training set are biased, the performance on examples with relative positions unseen during training is significantly degraded.

Extractive Question-Answering Position +1

How Well Do Multi-hop Reading Comprehension Models Understand Date Information?

1 code implementation11 Oct 2022 Xanh Ho, Saku Sugawara, Akiko Aizawa

Other results reveal that our probing questions can help to improve the performance of the models (e. g., by +10. 3 F1) on the main QA task and our dataset can be used for data augmentation to improve the robustness of the models.

Data Augmentation Multi-Hop Reading Comprehension +1

Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts

1 code implementation29 Sep 2022 Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa

Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.

Bias Detection Sentence

Do BERTs Learn to Use Browser User Interface? Exploring Multi-Step Tasks with Unified Vision-and-Language BERTs

1 code implementation15 Mar 2022 Taichi Iki, Akiko Aizawa

We develop task pages with and without page transitions and propose a BERT extension for the framework.

Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap

1 code implementation23 Sep 2021 Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question-context lexical overlap.

Data Augmentation Question Answering +3

Keyphrase Generation for Scientific Document Retrieval

1 code implementation ACL 2020 Florian Boudin, Ygor Gallina, Akiko Aizawa

Sequence-to-sequence models have lead to significant progress in keyphrase generation, but it remains unknown whether they are reliable enough to be beneficial for document retrieval.

Keyphrase Generation Retrieval

Maintaining Common Ground in Dynamic Environments

1 code implementation29 May 2021 Takuma Udagawa, Akiko Aizawa

Common grounding is the process of creating and maintaining mutual understandings, which is a critical aspect of sophisticated human communication.

End-To-End Dialogue Modelling Goal-Oriented Dialogue Systems +1

Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models

1 code implementation EMNLP 2021 Taichi Iki, Akiko Aizawa

A method for creating a vision-and-language (V&L) model is to extend a language model through structural modifications and V&L pre-training.

Language Modelling Machine Reading Comprehension +1

Communicative-Function-Based Sentence Classification for Construction of an Academic Formulaic Expression Database

no code implementations EACL 2021 Kenichi Iwatsuki, Akiko Aizawa

In this study, we considered a fully automated construction of a CF-labelled FE database using the top{--}down approach, in which the CF labels are first assigned to sentences, and then the FEs are extracted.

Sentence Sentence Classification

Multi-sense embeddings through a word sense disambiguation process

1 code implementation21 Jan 2021 Terry Ruas, William Grosky, Akiko Aizawa

Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data.

Natural Language Understanding Word Embeddings +2

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

1 code implementation COLING 2020 Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa

The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model.

Multi-hop Question Answering Question Answering

Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems

1 code implementation COLING 2020 Vitou Phy, Yang Zhao, Akiko Aizawa

For instance, specificity is mandatory in a food-ordering dialogue task, whereas fluency is preferred in a language-teaching dialogue system.

Dialogue Evaluation Semantic Similarity +2

Language-Conditioned Feature Pyramids for Visual Selection Tasks

1 code implementation Findings of the Association for Computational Linguistics 2020 Taichi Iki, Akiko Aizawa

However, few models consider the fusion of linguistic features with multiple visual features with different sizes of receptive fields, though the proper size of the receptive field of visual features intuitively varies depending on expressions.

Referring Expression Referring Expression Comprehension

Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers

no code implementations18 Jun 2020 Kenichi Iwatsuki, Florian Boudin, Akiko Aizawa

We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence.


An Evaluation Dataset for Identifying Communicative Functions of Sentences in English Scholarly Papers

no code implementations LREC 2020 Kenichi Iwatsuki, Florian Boudin, Akiko Aizawa

Formulaic expressions, such as {`}in this paper we propose{'}, are used by authors of scholarly papers to perform communicative functions; the communicative function of the present example is {`}stating the aim of the paper{'}.


Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation

1 code implementation ACL 2021 Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

While most existing QAG methods aim to improve the quality of synthetic examples, we conjecture that diversity-promoting QAG can mitigate the sparsity of training sets and lead to better robustness.

Data Augmentation Machine Reading Comprehension +1

Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations

1 code implementation7 Feb 2020 Andre Greiner-Petter, Moritz Schubotz, Fabian Mueller, Corinna Breitinger, Howard S. Cohl, Akiko Aizawa, Bela Gipp

The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e. g., linking $P_{n}^{(\alpha, \beta)}\!\left(x\right)$ with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems.

Information Retrieval Math +2

From Natural Language Instructions to Complex Processes: Issues in Chaining Trigger Action Rules

no code implementations8 Jan 2020 Nobuhiro Ito, Yuya Suzuki, Akiko Aizawa

A natural language interface for such automation is expected as an elemental technology for the IPA realization.

Semantic Parsing

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.

Benchmarking Machine Reading Comprehension +1

An Annotated Corpus of Reference Resolution for Interpreting Common Grounding

1 code implementation18 Nov 2019 Takuma Udagawa, Akiko Aizawa

Common grounding is the process of creating, repairing and updating mutual understandings, which is a fundamental aspect of natural language conversation.

Coreference Resolution Goal-Oriented Dialog +2

A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context

1 code implementation8 Jul 2019 Takuma Udagawa, Akiko Aizawa

Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.

Dialogue Understanding Goal-Oriented Dialog +1

Unsupervised Rewriter for Multi-Sentence Compression

no code implementations ACL 2019 Yang Zhao, Xiaoyu Shen, Wei Bi, Akiko Aizawa

First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression.

Sentence Sentence Compression

Why Machines Cannot Learn Mathematics, Yet

no code implementations20 May 2019 André Greiner-Petter, Terry Ruas, Moritz Schubotz, Akiko Aizawa, William Grosky, Bela Gipp

Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods.

BIG-bench Machine Learning Information Retrieval +1

The Architecture of Mr. DLib's Scientific Recommender-System API

no code implementations26 Nov 2018 Joeran Beel, Andrew Collins, Akiko Aizawa

In this paper, we introduce Mr. DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of academic products to easily integrate a scientific recommender system into their products.

Recommendation Systems

Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning

2 code implementations3 Sep 2018 Junjun Jiang, Yi Yu, Suhua Tang, Jiayi Ma, Akiko Aizawa, Kiyoharu Aizawa

To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL).

Face Hallucination Hallucination +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 +1

Using Formulaic Expressions in Writing Assistance Systems

1 code implementation COLING 2018 Kenichi Iwatsuki, Akiko Aizawa

Formulaic expressions (FEs) used in scholarly papers, such as {`}there has been little discussion about{'}, are helpful for non-native English speakers.


A Language Model based Evaluator for Sentence Compression

no code implementations ACL 2018 Yang Zhao, Zhiyuan Luo, Akiko Aizawa

We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator.

Language Modelling reinforcement-learning +4

Category-Based Deep CCA for Fine-Grained Venue Discovery from Multimodal Data

no code implementations8 May 2018 Yi Yu, Suhua Tang, Kiyoharu Aizawa, Akiko Aizawa

Given a photo as input, this model performs (i) exact venue search (find the venue where the photo was taken), and (ii) group venue search (find relevant venues with the same category as that of the photo), by the cross-modal correlation between the input photo and textual description of venues.

Cross-Modal Retrieval Retrieval

A Study of Position Bias in Digital Library Recommender Systems

no code implementations19 Feb 2018 Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, Joeran Beel

We conduct a study in a real-world recommender system that delivered ten million related-article recommendations to the users of the digital library Sowiport, and the reference manager JabRef.

Position Recommendation Systems

SideNoter: Scholarly Paper Browsing System based on PDF Restructuring and Text Annotation

no code implementations COLING 2016 Takeshi Abekawa, Akiko Aizawa

In this paper, we discuss our ongoing efforts to construct a scientific paper browsing system that helps users to read and understand advanced technical content distributed in PDF.

text annotation

Typed Entity and Relation Annotation on Computer Science Papers

1 code implementation LREC 2016 Yuka Tateisi, Tomoko Ohta, Sampo Pyysalo, Yusuke Miyao, Akiko Aizawa

In our scheme, mentions of entities are annotated with ontology-based types, and the roles of the entities are annotated as relations with other entities described in the text.


English-to-Japanese Translation vs. Dictation vs. Post-editing: Comparing Translation Modes in a Multilingual Setting

no code implementations LREC 2016 Michael Carl, Akiko Aizawa, Masaru Yamada

Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production.

Machine Translation Translation

Corpus for Coreference Resolution on Scientific Papers

1 code implementation LREC 2014 Panot Chaimongkol, Akiko Aizawa, Yuka Tateisi

Through these comparisons, we have demonstrated quantitatively that our manually annotated corpus differs from a general-domain corpus, which suggests deep differences between general-domain texts and scientific texts and which shows that different approaches can be made to tackle coreference resolution for general texts and scientific texts.

coreference-resolution Optical Character Recognition (OCR)

Annotation of Computer Science Papers for Semantic Relation Extrac-tion

no code implementations LREC 2014 Yuka Tateisi, Yo Shidahara, Yusuke Miyao, Akiko Aizawa

We designed a new annotation scheme for formalising relation structures in research papers, through the investigation of computer science papers.

Information Retrieval Relation +2

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