Search Results for author: Teruko Mitamura

Found 46 papers, 13 papers with code

Formulation Comparison for Timeline Construction using LLMs

1 code implementation1 Mar 2024 Kimihiro Hasegawa, Nikhil Kandukuri, Susan Holm, Yukari Yamakawa, Teruko Mitamura

Considering that identifying temporal orders of events is a core subtask in timeline construction, we further benchmark open LLMs on existing event temporal ordering datasets to gain a robust understanding of their capabilities.

Hierarchical Event Grounding

no code implementations8 Feb 2023 Jiefu Ou, Adithya Pratapa, Rishubh Gupta, Teruko Mitamura

In this work, we present an extension to the event grounding task that requires tackling hierarchical event structures from the KB.


Multilingual Event Linking to Wikidata

1 code implementation NAACL (MIA) 2022 Adithya Pratapa, Rishubh Gupta, Teruko Mitamura

On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020).

Domain Generalization

Cross-document Event Identity via Dense Annotation

1 code implementation CoNLL (EMNLP) 2021 Adithya Pratapa, Zhengzhong Liu, Kimihiro Hasegawa, Linwei Li, Yukari Yamakawa, Shikun Zhang, Teruko Mitamura

To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface.

A Survey of Data Augmentation Approaches for NLP

1 code implementation Findings (ACL) 2021 Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy

In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.

Data Augmentation

NAREOR: The Narrative Reordering Problem

1 code implementation14 Apr 2021 Varun Gangal, Steven Y. Feng, Malihe Alikhani, Teruko Mitamura, Eduard Hovy

In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot.

A Data-Centric Framework for Composable NLP Workflows

1 code implementation EMNLP 2020 Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Haoying Zhang, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu

Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.

Retrieval Text Retrieval

Understanding the Role of Scene Graphs in Visual Question Answering

no code implementations14 Jan 2021 Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, Chenhui Chu

Visual Question Answering (VQA) is of tremendous interest to the research community with important applications such as aiding visually impaired users and image-based search.

Graph Generation Question Answering +2

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation

no code implementations WS 2018 Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Ch, Khyathi u, Teruko Mitamura, Eric Nyberg

The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine.

Abstractive Text Summarization Answer Generation +4

Automatic Event Salience Identification

1 code implementation EMNLP 2018 Zhengzhong Liu, Chenyan Xiong, Teruko Mitamura, Eduard Hovy

Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e. g., scripts and frame structures).

Open-Domain Event Detection using Distant Supervision

1 code implementation COLING 2018 Jun Araki, Teruko Mitamura

This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation.

Event Detection Open-Domain Question Answering

BioAMA: Towards an End to End BioMedical Question Answering System

no code implementations WS 2018 Vasu Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, Teruko Mitamura

In this paper, we present a novel Biomedical Question Answering system, BioAMA: {``}Biomedical Ask Me Anything{''} on task 5b of the annual BioASQ challenge.

Natural Language Inference NER +4

Event Detection Using Frame-Semantic Parser

no code implementations WS 2017 Evangelia Spiliopoulou, Eduard Hovy, Teruko Mitamura

Recent methods for Event Detection focus on Deep Learning for automatic feature generation and feature ranking.

Event Detection General Classification +1

Resources for the Detection of Conventionalized Metaphors in Four Languages

no code implementations LREC 2014 Lori Levin, Teruko Mitamura, Brian MacWhinney, Davida Fromm, Jaime Carbonell, Weston Feely, Robert Frederking, Anatole Gershman, Carlos Ramirez

The extraction rules operate on the output of a dependency parser and identify the grammatical configurations (such as a verb with a prepositional phrase complement) that are likely to contain conventional metaphors.

Diversifiable Bootstrapping for Acquiring High-Coverage Paraphrase Resource

no code implementations LREC 2012 Hideki Shima, Teruko Mitamura

Recognizing similar or close meaning on different surface form is a common challenge in various Natural Language Processing and Information Access applications.

Information Retrieval Machine Translation +3

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