Yunmo Chen


2021

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Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Mahsa Yarmohammadi | Shijie Wu | Marc Marone | Haoran Xu | Seth Ebner | Guanghui Qin | Yunmo Chen | Jialiang Guo | Craig Harman | Kenton Murray | Aaron Steven White | Mark Dredze | Benjamin Van Durme
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of “train on English, run on any language”, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.

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LOME: Large Ontology Multilingual Extraction
Patrick Xia | Guanghui Qin | Siddharth Vashishtha | Yunmo Chen | Tongfei Chen | Chandler May | Craig Harman | Kyle Rawlins | Aaron Steven White | Benjamin Van Durme
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.

2020

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Hierarchical Entity Typing via Multi-level Learning to Rank
Tongfei Chen | Yunmo Chen | Benjamin Van Durme
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.

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Reading the Manual: Event Extraction as Definition Comprehension
Yunmo Chen | Tongfei Chen | Seth Ebner | Aaron Steven White | Benjamin Van Durme
Proceedings of the Fourth Workshop on Structured Prediction for NLP

We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, “Some person was born in some location at some time.” We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.

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Joint Modeling of Arguments for Event Understanding
Yunmo Chen | Tongfei Chen | Benjamin Van Durme
Proceedings of the First Workshop on Computational Approaches to Discourse

We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.