Xiaodong Yu


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Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents
Yanfei Dong | Lambert Deng | Jiazheng Zhang | Xiaodong Yu | Ting Lin | Francesco Gelli | Soujanya Poria | Wee Sun Lee
Findings of the Association for Computational Linguistics: EACL 2023

Documents that consist of diverse templates and exhibit complex spatial structures pose a challenge for document entity classification. We propose KNN-Former, which incorporates a new kind of spatial bias in attention calculation based on the K-nearest-neighbor (KNN) graph of document entities. We limit entities’ attention only to their local radius defined by the KNN graph. We also use combinatorial matching to address the one-to-one mapping property that exists in many documents, where one field has only one corresponding entity. Moreover, our method is highly parameter-efficient compared to existing approaches in terms of the number of trainable parameters. Despite this, experiments across various datasets show our method outperforms baselines in most entity types. Many real-world documents exhibit combinatorial properties which can be leveraged as inductive biases to improve extraction accuracy, but existing datasets do not cover these documents. To facilitate future research into these types of documents, we release a new ID document dataset that covers diverse templates and languages. We also release enhanced annotations for an existing dataset.

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Event Linking: Grounding Event Mentions to Wikipedia
Xiaodong Yu | Wenpeng Yin | Nitish Gupta | Dan Roth
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines the Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather the training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system, EVELINK, to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitations of the current model. Overall, as our analysis shows, Event Linking is a challenging and essential task requiring more effort from the community.


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Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Ben Zhou | Kyle Richardson | Xiaodong Yu | Dan Roth
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems. However, despite the many datasets and resources built as part of this effort, the majority have small-scale annotations and limited scope, which is insufficient to solve general decomposition tasks. In this paper, we look at large-scale intermediate pre-training of decomposition-based transformers using distant supervision from comparable texts, particularly large-scale parallel news. We show that with such intermediate pre-training, developing robust decomposition-based models for a diverse range of tasks becomes more feasible. For example, on semantic parsing, our model, DecompT5, improves 20% to 30% on two datasets, Overnight and TORQUE, over the baseline language model. We further use DecompT5 to build a novel decomposition-based QA system named DecompEntail, improving over state-of-the-art models, including GPT-3, on both HotpotQA and StrategyQA by 8% and 4%, respectively.

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Pairwise Representation Learning for Event Coreference
Xiaodong Yu | Wenpeng Yin | Dan Roth
Proceedings of the 11th Joint Conference on Lexical and Computational Semantics

Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets. These tasks are typically formulated as (binary) classification problems over independently induced representations of the text snippets. In this work, we develop a Pairwise Representation Learning (PairwiseRL) scheme for the event mention pairs, in which we jointly encode a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. Furthermore, our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments. We show that PairwiseRL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks. We also conduct in-depth analysis in terms of the improvement and the limitation of pairwise representation so as to provide insights for future work.

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Capturing the Content of a Document through Complex Event Identification
Zheng Qi | Elior Sulem | Haoyu Wang | Xiaodong Yu | Dan Roth
Proceedings of the 11th Joint Conference on Lexical and Computational Semantics

Granular events, instantiated in a document by predicates, can usually be grouped into more general events, called complex events. Together, they capture the major content of the document. Recent work grouped granular events by defining event regions, filtering out sentences that are irrelevant to the main content. However, this approach assumes that a given complex event is always described in consecutive sentences, which does not always hold in practice. In this paper, we introduce the task of complex event identification. We address this task as a pipeline, first predicting whether two granular events mentioned in the text belong to the same complex event, independently of their position in the text, and then using this to cluster them into complex events. Due to the difficulty of predicting whether two granular events belong to the same complex event in isolation, we propose a context-augmented representation learning approach CONTEXTRL that adds additional context to better model the pairwise relation between granular events. We show that our approach outperforms strong baselines on the complex event identification task and further present a promising case study exploring the effectiveness of using complex events as input for document-level argument extraction.


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RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
Haoyang Wen | Ying Lin | Tuan Lai | Xiaoman Pan | Sha Li | Xudong Lin | Ben Zhou | Manling Li | Haoyu Wang | Hongming Zhang | Xiaodong Yu | Alexander Dong | Zhenhailong Wang | Yi Fung | Piyush Mishra | Qing Lyu | Dídac Surís | Brian Chen | Susan Windisch Brown | Martha Palmer | Chris Callison-Burch | Carl Vondrick | Jiawei Han | Dan Roth | Shih-Fu Chang | Heng Ji
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video). The system advances state-of-the-art from two aspects: (1) extending from sentence-level event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhance the extraction output. We have made the dockerlized system publicly available for research purpose at GitHub, with a demo video.


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Design Challenges in Low-resource Cross-lingual Entity Linking
Xingyu Fu | Weijia Shi | Xiaodong Yu | Zian Zhao | Dan Roth
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia’s interlanguage links and thus suffer when the foreign language’s Wikipedia is small. We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages, QuEL shows an average increase of 25% in gold candidate recall and of 13% in end-to-end linking accuracy over state-of-the-art baselines.


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On the Strength of Character Language Models for Multilingual Named Entity Recognition
Xiaodong Yu | Stephen Mayhew | Mark Sammons | Dan Roth
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and nonname tokens in text, nor whether this property holds across multiple languages. This paper analyzes the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens. We demonstrate that CLMs provide a simple and powerful model for capturing these differences, identifying named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an off-the-shelf NER system for multiple languages.

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CogCompNLP: Your Swiss Army Knife for NLP
Daniel Khashabi | Mark Sammons | Ben Zhou | Tom Redman | Christos Christodoulopoulos | Vivek Srikumar | Nicholas Rizzolo | Lev Ratinov | Guanheng Luo | Quang Do | Chen-Tse Tsai | Subhro Roy | Stephen Mayhew | Zhili Feng | John Wieting | Xiaodong Yu | Yangqiu Song | Shashank Gupta | Shyam Upadhyay | Naveen Arivazhagan | Qiang Ning | Shaoshi Ling | Dan Roth
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)