64 citations · 227 across the 16 of their papers we have counts for
17 papers
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi +9
Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity m…
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
Akari Asai, Shayne Longpre, Jungo Kasai +6
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typo…
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada +2
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities. The advantage of using entity supervision is…
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
Ryokan Ri, Ikuya Yamada, Yoshimasa Tsuruoka
Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, existi…
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka +1
We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained langu…
Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Ikuya Yamada, Akari Asai, Hannaneh Hajishirzi
Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. Howev…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo +2
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based…
Neural Attentive Bag-of-Entities Model for Text Classification
Ikuya Yamada, Hiroyuki Shindo
This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide…
Global Entity Disambiguation with BERT
Ikuya Yamada, Koki Washio, Hiroyuki Shindo +1
We propose a global entity disambiguation (ED) model based on BERT. To capture global contextual information for ED, our model treats not only words but also entities as input toke…
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
Ikuya Yamada, Akari Asai, Jin Sakuma +4
The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this p…
Representation Learning of Entities and Documents from Knowledge Base Descriptions
Ikuya Yamada, Hiroyuki Shindo, Yoshiyasu Takefuji
In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document i…
Studio Ousia's Quiz Bowl Question Answering System
Ikuya Yamada, Ryuji Tamaki, Hiroyuki Shindo +1
In this chapter, we describe our question answering system, which was the winning system at the Human-Computer Question Answering (HCQA) Competition at the Thirty-first Annual Conf…
Named Entity Disambiguation for Noisy Text
Yotam Eshel, Noam Cohen, Kira Radinsky +3
We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly n…
Learning Distributed Representations of Texts and Entities from Knowledge Base
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda +1
We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to…
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
Ikuya Yamada, Motoki Sato, Hiroyuki Shindo
This paper describes our approach for the triple scoring task at the WSDM Cup 2017. The task required participants to assign a relevance score for each pair of entities and their t…
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda +1
Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia…