Ikuya Yamada


2020

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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Ikuya Yamada | Akari Asai | Hiroyuki Shindo | Hideaki Takeda | Yuji Matsumoto
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering). Our source code and pretrained representations are available at https://github.com/studio-ousia/luke.

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Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
Ikuya Yamada | Akari Asai | Jin Sakuma | Hiroyuki Shindo | Hideaki Takeda | Yoshiyasu Takefuji | Yuji Matsumoto
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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 paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io/.

2019

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Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering
Eric Wallace | Pedro Rodriguez | Shi Feng | Ikuya Yamada | Jordan Boyd-Graber
Transactions of the Association for Computational Linguistics, Volume 7

Adversarial evaluation stress-tests a model’s understanding of natural language. Because past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human- in-the-loop adversarial generation, where human authors are guided to break models. We aid the authors with interpretations of model predictions through an interactive user interface. We apply this generation framework to a question answering task called Quizbowl, where trivia enthusiasts craft adversarial questions. The resulting questions are validated via live human–computer matches: Although the questions appear ordinary to humans, they systematically stump neural and information retrieval models. The adversarial questions cover diverse phenomena from multi-hop reasoning to entity type distractors, exposing open challenges in robust question answering.

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Neural Attentive Bag-of-Entities Model for Text Classification
Ikuya Yamada | Hiroyuki Shindo
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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 unambiguous and relevant semantic signals that are beneficial for text classification. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.

2018

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Representation Learning of Entities and Documents from Knowledge Base Descriptions
Ikuya Yamada | Hiroyuki Shindo | Yoshiyasu Takefuji
Proceedings of the 27th International Conference on Computational Linguistics

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 in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.

2017

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Named Entity Disambiguation for Noisy Text
Yotam Eshel | Noam Cohen | Kira Radinsky | Shaul Markovitch | Ikuya Yamada | Omer Levy
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

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 noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.

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Segment-Level Neural Conditional Random Fields for Named Entity Recognition
Motoki Sato | Hiroyuki Shindo | Ikuya Yamada | Yuji Matsumoto
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.

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Learning Distributed Representations of Texts and Entities from Knowledge Base
Ikuya Yamada | Hiroyuki Shindo | Hideaki Takeda | Yoshiyasu Takefuji
Transactions of the Association for Computational Linguistics, Volume 5

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 predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.

2016

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Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
Ikuya Yamada | Hiroyuki Shindo | Hideaki Takeda | Yoshiyasu Takefuji
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

2015

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Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking
Ikuya Yamada | Hideaki Takeda | Yoshiyasu Takefuji
Proceedings of the Workshop on Noisy User-generated Text