Yizhou Sun


2022

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Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
Zijie Huang | Zheng Li | Haoming Jiang | Tianyu Cao | Hanqing Lu | Bing Yin | Karthik Subbian | Yizhou Sun | Wei Wang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages. However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights. Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AGA

2021

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UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference
Kewei Cheng | Ziqing Yang | Ming Zhang | Yizhou Sun
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Knowledge graph inference has been studied extensively due to its wide applications. It has been addressed by two lines of research, i.e., the more traditional logical rule reasoning and the more recent knowledge graph embedding (KGE). Several attempts have been made to combine KGE and logical rules for better knowledge graph inference. Unfortunately, they either simply treat logical rules as additional constraints into KGE loss or use probabilistic model to approximate the exact logical inference (i.e., MAX-SAT). Even worse, both approaches need to sample ground rules to tackle the scalability issue, as the total number of ground rules is intractable in practice, making them less effective in handling logical rules. In this paper, we propose a novel framework UniKER to address these challenges by restricting logical rules to be definite Horn rules, which can fully exploit the knowledge in logical rules and enable the mutual enhancement of logical rule-based reasoning and KGE in an extremely efficient way. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.

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Relation-Guided Pre-Training for Open-Domain Question Answering
Ziniu Hu | Yizhou Sun | Kai-Wei Chang
Findings of the Association for Computational Linguistics: EMNLP 2021

Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbalanced in some types of relations, which hurts the generalization performance over questions with long-tail relations. To remedy this problem, in this paper, we propose a Relation-Guided Pre-Training (RGPT-QA) framework. We first generate a relational QA dataset covering a wide range of relations from both the Wikidata triplets and Wikipedia hyperlinks. We then pre-train a QA model to infer the latent relations from the question, and then conduct extractive QA to get the target answer entity. We demonstrate that by pre-training with propoed RGPT-QA techique, the popular open-domain QA model, Dense Passage Retriever (DPR), achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions. Particularly, we show that RGPT-QA improves significantly on questions with long-tail relations.

2020

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Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer
Xuelu Chen | Muhao Chen | Changjun Fan | Ankith Uppunda | Yizhou Sun | Carlo Zaniolo
Findings of the Association for Computational Linguistics: EMNLP 2020

Predicting missing facts in a knowledge graph(KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works us-ing KG embeddings. While existing KG embedding approaches mainly learn and predict facts within a single KG, a more plausible solution would benefit from the knowledge in multiple language-specific KGs, considering that different KGs have their own strengths and limitations on data quality and coverage. This is quite challenging since the transfer of knowledge among multiple independently maintained KGs is often hindered by the insufficiency of alignment information and inconsistency of described facts. In this paper, we propose kens, a novel framework for embedding learning and ensemble knowledge transfer across a number of language-specific KGs.KEnS embeds all KGs in a shared embedding space, where the association of entities is captured based on self-learning. Then, KEnS performs ensemble inference to com-bine prediction results from multiple language-specific embeddings, for which multiple en-semble techniques are investigated. Experiments on the basis of five real-world language-specific KGs show that, by effectively identifying and leveraging complementary knowledge, KEnS consistently improves state-of-the-art methods on KG completion.

2019

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Few-Shot Representation Learning for Out-Of-Vocabulary Words
Ziniu Hu | Ting Chen | Kai-Wei Chang | Yizhou Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Existing approaches for learning word embedding often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus emerge frequently. How to learn accurate representations of these words to augment a pre-trained embedding by only a few observations is a challenging research problem. In this paper, we formulate the learning of OOV embedding as a few-shot regression problem by fitting a representation function to predict an oracle embedding vector (defined as embedding trained with abundant observations) based on limited contexts. Specifically, we propose a novel hierarchical attention network-based embedding framework to serve as the neural regression function, in which the context information of a word is encoded and aggregated from K observations. Furthermore, we propose to use Model-Agnostic Meta-Learning (MAML) for adapting the learned model to the new corpus fast and robustly. Experiments show that the proposed approach significantly outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embedding is utilized.

2015

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Context-aware Entity Morph Decoding
Boliang Zhang | Hongzhao Huang | Xiaoman Pan | Sujian Li | Chin-Yew Lin | Heng Ji | Kevin Knight | Zhen Wen | Yizhou Sun | Jiawei Han | Bulent Yener
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Successful Data Mining Methods for NLP
Jiawei Han | Heng Ji | Yizhou Sun
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts

2014

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Be Appropriate and Funny: Automatic Entity Morph Encoding
Boliang Zhang | Hongzhao Huang | Xiaoman Pan | Heng Ji | Kevin Knight | Zhen Wen | Yizhou Sun | Jiawei Han | Bulent Yener
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Resolving Entity Morphs in Censored Data
Hongzhao Huang | Zhen Wen | Dian Yu | Heng Ji | Yizhou Sun | Jiawei Han | He Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)