Search Results for author: Akiko Aizawa

Found 67 papers, 23 papers with code

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

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 Natural Language Processing

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 +2

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

1 code implementation Findings (EMNLP) 2021 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

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 +2

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

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 Classification

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

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

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 +1

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 Recommendation Systems

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

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 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.

Information Retrieval

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

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

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 +1

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

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.

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.

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

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.

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 Extraction

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

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