Papers by Ikuya Yamada
Dynamic Injection of Entity Knowledge into Dense Retrievers (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Dense retrievers struggle with queries involving less-frequent entities due to limited entity knowledge. |
| Approach: | They propose a BERT-based retriever enhanced with a context-entity attention layer and dynamically updatable entity embeddings. |
| Outcome: | The proposed retriever incorporates external entity knowledge without retraining. |
EASE: Entity-Aware Contrastive Learning of Sentence Embedding (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for learning sentence embeddings are fine-tuning general-purpose pretrained models with a particular training supervision. |
| Approach: | They propose a method for learning sentence embeddings via contrastive learning between sentences and related entities. |
| Outcome: | The proposed method outperforms baseline methods in multilingual settings on a variety of tasks. |
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation (2024.findings-eacl)
Copied to clipboard
Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
| Challenge: | et al., 2006) considers geographic relatedness among geo-entity mentions in document-level geoparsing. |
| Approach: | They present a Japanese travelogue dataset that considers geographic relatedness among geo-entity mentions. |
| Outcome: | The proposed dataset includes 200 travelogue documents with rich geo-entity information . it shows that human activities, mobility, and events are often described with natural language expressions of locations or geographic entities (geo-entities) |
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for improving multilingual models only use entity information in pretraining and do not explicitly use entities in downstream tasks. |
| Approach: | They propose to leverage Wikipedia entity representations for downstream tasks . they train a multilingual language model with 24 languages with entity representation . |
| Outcome: | The proposed model outperforms word-based models in cross-lingual transfer tasks. |
Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021.acl-short)
Copied to clipboard
| Challenge: | Open-domain question answering systems often require large memory to run because of the massive size of their passage index. |
| Approach: | They propose a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever to represent the passage index using compact binary codes. |
| Outcome: | The proposed model significantly reduces memory cost from 65GB to 2GB without loss of accuracy on two open-domain question answering benchmarks. |
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia (2020.emnlp-demos)
Copied to clipboard
Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto
| Challenge: | Existing tools for learning the embeddings of words and entities from Wikipedia are not yet available. |
| Approach: | They propose a Python-based tool for learning Wikipedia embeddings from Wikipedia . they use a Wikipedia dump file as an argument to issue a single command . |
| Outcome: | The proposed tool achieves state-of-the-art results on the KORE entity relatedness dataset and competitive results on benchmark datasets. |
Entity Embedding Completion for Wide-Coverage Entity Disambiguation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing state-of-the-art ED models do not address out-of vocabulary entities that are absent from training data. |
| Approach: | They propose to extend a state-of-the-art ED model by dynamically computing embeddings of out-ofvocabulary entities by using entity descriptions and mention contexts. |
| Outcome: | The proposed model performs comparable to existing models whose embeddings are trained for all candidate entities as well as embedd-free models. |
Representation Learning of Entities and Documents from Knowledge Base Descriptions (C18-1)
Copied to clipboard
| Challenge: | Using documents extracted from Wikipedia, we train a neural network model that learns distributed representations of entities and documents directly from a knowledge base. |
| Approach: | They propose a neural network model that learns distributed representations of entities from a knowledge base. |
| Outcome: | The proposed model performs state-of-the-art on fine-grained entity typing and multiclass text classification tasks. |
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for entity representations do not capture information in a knowledge base, and cannot represent entities that do not exist in the KB. |
| Approach: | They propose a pretrained contextualized representation of words and entities based on the bidirectional transformer. |
| Outcome: | The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. |
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing language adaptation methods overlook the benefits of cross-lingual supervision. |
| Approach: | They propose a language adaptation tuning method that augments target corpus with English entity names and trains the model using left-to-right language modeling. |
| Outcome: | The proposed method shows significant performance gains across non-English languages. |
Global Entity Disambiguation with BERT (2022.naacl-main)
Copied to clipboard
| Challenge: | Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base. |
| Approach: | They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia . |
| Outcome: | The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time. |