Bonan Min


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Cross-Document Event Coreference Resolution: Instruct Humans or Instruct GPT?
Jin Zhao | Nianwen Xue | Bonan Min
Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

This paper explores utilizing Large Language Models (LLMs) to perform Cross-Document Event Coreference Resolution (CDEC) annotations and evaluates how they fare against human annotators with different levels of training. Specifically, we formulate CDEC as a multi-category classification problem on pairs of events that are represented as decontextualized sentences, and compare the predictions of GPT-4 with the judgment of fully trained annotators and crowdworkers on the same data set. Our study indicates that GPT-4 with zero-shot learning outperformed crowd-workers by a large margin and exhibits a level of performance comparable to trained annotators. Upon closer analysis, GPT-4 also exhibits tendencies of being overly confident, and force annotation decisions even when such decisions are not warranted due to insufficient information. Our results have implications on how to perform complicated annotations such as CDEC in the age of LLMs, and show that the best way to acquire such annotations might be to combine the strengths of LLMs and trained human annotators in the annotation process, and using untrained or undertrained crowdworkers is no longer a viable option to acquire high-quality data to advance the state of the art for such problems.

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Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training
Amir Pouran Ben Veyseh | Franck Dernoncourt | Bonan Min | Thien Nguyen
Findings of the Association for Computational Linguistics: ACL 2023

Relation Extraction (RE) is the task of identifying semantic relation between real-world entities mentioned in text. Despite significant progress in RE research, a remaining challenge for RE concerns the lack of training data for data-hungry deep learning models. Cost of annotation and difficulty of the task are among hindrance to collect a large-scale RE dataset in different domains. To address this limitation, we propose a novel framework to automatically generate labeled data for RE. Our framework presents the pre-trained language model GPT-2 for data generation. In addition, to optimize the generated samples for an RE model, we introduce a meta learning approach to allow the GPT-2 model to be updated during the training process for RE. In particular, to leverage the feedback from the RE model to improve the data generation from GPT-2, we propose a novel reward function to update the GPT-2 model with REINFORCE, seeking to promote the similarity of the RE loss function’s gradients computed for generated data and a meta development set. We conduct extensive experiments on two benchmark datasets to produce state-of-the-art performance for RE.

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A Multi-Modal Multilingual Benchmark for Document Image Classification
Yoshinari Fujinuma | Siddharth Varia | Nishant Sankaran | Srikar Appalaraju | Bonan Min | Yogarshi Vyas
Findings of the Association for Computational Linguistics: EMNLP 2023

Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated multilingual datasets WIKI-DOC and MULTIEURLEX-DOC that overcome these limitations. We further undertake a comprehensive study of popular visually-rich document understanding or Document AI models in previously untested setting in document image classification such as 1) multi-label classification, and 2) zero-shot cross-lingual transfer setup. Experimental results show limitations of multilingual Document AI models on cross-lingual transfer across typologically distant languages. Our datasets and findings open the door for future research into improving Document AI models.

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Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
Alexander Hanbo Li | Mingyue Shang | Evangelia Spiliopoulou | Jie Ma | Patrick Ng | Zhiguo Wang | Bonan Min | William Yang Wang | Kathleen McKeown | Vittorio Castelli | Dan Roth | Bing Xiang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we present a novel approach for data-to-text generation that addresses the limitations of current methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios by providing a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. We demonstrate that our proposed approach can effectively adapt to new structured forms, and can improve performance in comparison to current methods. For example, our method resulted in a 66% improvement in zero-shot BLEU scores when transferring models trained on table inputs to a knowledge graph dataset. Our proposed method is an important step towards a more general data-to-text generation framework.


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Generating Complement Data for Aspect Term Extraction with GPT-2
Amir Pouran Ben Veyseh | Franck Dernoncourt | Bonan Min | Thien Huu Nguyen
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

Aspect Term Extraction (ATE) is the task of identifying the word(s) in a review text toward which the author express an opinion. A major challenges for ATE involve data scarcity that hinder the training of deep sequence taggers to identify rare targets. To overcome these issues, we propose a novel method to better exploit the available labeled data for ATE by computing effective complement sentences to augment the input data and facilitate the aspect term prediction. In particular, we introduce a multistep training procedure that first obtains optimal complement representations and sentences for training data with respect to a deep ATE model. Afterward, we fine-tune the generative language model GPT-2 to allow complement sentence generation at test data. The REINFORCE algorithm is employed to incorporate different expected properties into the reward function to perform the fine-tuning. We perform extensive experiments on the benchmark datasets to demonstrate the benefits of the proposed method that achieve the state-of-the-art performance on different datasets.

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Document-Level Event Argument Extraction via Optimal Transport
Amir Pouran Ben Veyseh | Minh Van Nguyen | Franck Dernoncourt | Bonan Min | Thien Nguyen
Findings of the Association for Computational Linguistics: ACL 2022

Event Argument Extraction (EAE) is one of the sub-tasks of event extraction, aiming to recognize the role of each entity mention toward a specific event trigger. Despite the success of prior works in sentence-level EAE, the document-level setting is less explored. In particular, whereas syntactic structures of sentences have been shown to be effective for sentence-level EAE, prior document-level EAE models totally ignore syntactic structures for documents. Hence, in this work, we study the importance of syntactic structures in document-level EAE. Specifically, we propose to employ Optimal Transport (OT) to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. Furthermore, we propose a novel regularization technique to explicitly constrain the contributions of unrelated context words in the final prediction for EAE. We perform extensive experiments on the benchmark document-level EAE dataset RAMS that leads to the state-of-the-art performance. Moreover, our experiments on the ACE 2005 dataset reveals the effectiveness of the proposed model in the sentence-level EAE by establishing new state-of-the-art results.

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Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
Oscar Sainz | Itziar Gonzalez-Dios | Oier Lopez de Lacalle | Bonan Min | Eneko Agirre
Findings of the Association for Computational Linguistics: NAACL 2022

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as a Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few-shot settings thanks to pre-trained entailment models. The fact that relations in current RE datasets are easily verbalized casts doubts on whether entailment would be effective in more complex tasks. In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents, respectively, while achieving the same performance as with full training. More importantly, we show that recasting EAE as entailment alleviates the dependency on schemas, which has been a roadblock for transferring annotations between domains. Thanks to entailment, the multi-source transfer between ACE and WikiEvents further reduces annotation down to 10% and 5% (respectively) of the full training without transfer. Our analysis shows that key to good results is the use of several entailment datasets to pre-train the entailment model. Similar to previous approaches, our method requires a small amount of effort for manual verbalization: only less than 15 minutes per event argument types is needed; comparable results can be achieved from users of different level of expertise.

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Unsupervised Domain Adaptation for Joint Information Extraction
Nghia Ngo | Bonan Min | Thien Nguyen
Findings of the Association for Computational Linguistics: EMNLP 2022

Joint Information Extraction (JIE) aims to jointly solve multiple tasks in the Information Extraction pipeline (e.g., entity mention, event trigger, relation, and event argument extraction). Due to their ability to leverage task dependencies and avoid error propagation, JIE models have presented state-of-the-art performance for different IE tasks. However, an issue with current JIE methods is that they only focus on standard supervised learning setting where training and test data comes from the same domain. Cross-domain/domain adaptation learning with training and test data in different domains have not been explored for JIE, thus hindering the application of this technology to different domains in practice. To address this issue, our work introduces the first study to evaluate performance of JIE models in unsupervised domain adaptation setting. In addition, we present a novel method to induce domain-invariant representations for the tasks in JIE, called Domain Adaptation for Joint Information Extraction (DA4JIE). In DA4JIE, we propose an Instance-relational Domain Adaptation mechanism that seeks to align representations of task instances in JIE across domains through a generalized version of domain-adversarial learning approach. We further devise a Context-invariant Structure Learning technique to filter domain-specialized contextual information from induced representations to boost performance of JIE models in new domains. Extensive experiments and analyses demonstrate that DA4JIE can significantly improve out-of-domain performance for current state-of-the-art JIE systems for all IE tasks.

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Modal Dependency Parsing via Language Model Priming
Jiarui Yao | Nianwen Xue | Bonan Min
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The task of modal dependency parsing aims to parse a text into its modal dependency structure, which is a representation for the factuality of events in the text. We design a modal dependency parser that is based on priming pre-trained language models, and evaluate the parser on two data sets. Compared to baselines, we show an improvement of 2.6% in F-score for English and 4.6% for Chinese. To the best of our knowledge, this is also the first work on Chinese modal dependency parsing.

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Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies
Minh Van Nguyen | Bonan Min | Franck Dernoncourt | Thien Nguyen
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Event trigger detection, entity mention recognition, event argument extraction, and relation extraction are the four important tasks in information extraction that have been performed jointly (Joint Information Extraction - JointIE) to avoid error propagation and leverage dependencies between the task instances (i.e., event triggers, entity mentions, relations, and event arguments). However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and mean-field factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance. To overcome these limitations, we propose to induce a dependency graph among task instances from data to boost representation learning. To better capture dependencies between instance labels, we propose to directly estimate their joint distribution via Conditional Random Fields. Noise Contrastive Estimation is introduced to address the maximization of the intractable joint likelihood for model training. Finally, to improve the decoding with greedy or beam search in prior work, we present Simulated Annealing to better find the globally optimal assignment for instance labels at decoding time. Experimental results show that our proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages.

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ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
Oscar Sainz | Haoling Qiu | Oier Lopez de Lacalle | Eneko Agirre | Bonan Min
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this demonstration we introduce a new workflow where the analyst directly verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE. We present the design and implementation of a toolkit with a user interface, as well as experiments on four IE tasks that show that the system achieves very good performance at zero-shot learning using only 5–15 minutes per type of a user’s effort. Our demonstration system is open-sourced at A demonstration video is available at

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FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction
Minh Van Nguyen | Nghia Ngo | Bonan Min | Thien Nguyen
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

This paper presents FAMIE, a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction. FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration. This hinders the engagement, productivity, and efficiency of annotators. Based on the idea of using a small proxy network for fast data selection, we introduce a novel knowledge distillation mechanism to synchronize the proxy network with the main large model (i.e., BERT-based) to ensure the appropriateness of the selected annotation examples for the main model. Our AL framework can support multiple languages. The experiments demonstrate the advantages of FAMIE in terms of competitive performance and time efficiency for sequence labeling with AL. We publicly release our code ( and demo website ( A demo video for FAMIE is provided at:

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Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
Anastassia Loukina | Rashmi Gangadharaiah | Bonan Min
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

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Learning Cross-Task Dependencies for Joint Extraction of Entities, Events, Event Arguments, and Relations
Minh Van Nguyen | Bonan Min | Franck Dernoncourt | Thien Nguyen
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Extracting entities, events, event arguments, and relations (i.e., task instances) from text represents four main challenging tasks in information extraction (IE), which have been solved jointly (JointIE) to boost the overall performance for IE. As such, previous work often leverages two types of dependencies between the tasks, i.e., cross-instance and cross-type dependencies representing relatedness between task instances and correlations between information types of the tasks. However, the cross-task dependencies in prior work are not optimal as they are only designed manually according to some task heuristics. To address this issue, we propose a novel model for JointIE that aims to learn cross-task dependencies from data. In particular, we treat each task instance as a node in a dependency graph where edges between the instances are inferred through information from different layers of a pretrained language model (e.g., BERT). Furthermore, we utilize the Chow-Liu algorithm to learn a dependency tree between information types for JointIE by seeking to approximate the joint distribution of the types from data. Finally, the Chow-Liu dependency tree is used to generate cross-type patterns, serving as anchor knowledge to guide the learning of representations and dependencies between instances for JointIE. Experimental results show that our proposed model significantly outperforms strong JointIE baselines over four datasets with different languages.

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Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
Mihai Surdeanu | John Hungerford | Yee Seng Chan | Jessica MacBride | Benjamin Gyori | Andrew Zupon | Zheng Tang | Haoling Qiu | Bonan Min | Yan Zverev | Caitlin Hilverman | Max Thomas | Walter Andrews | Keith Alcock | Zeyu Zhang | Michael Reynolds | Steven Bethard | Rebecca Sharp | Egoitz Laparra
Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one which does not scale well. Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour, to quickly build or extend a taxonomy to better fit information needs.


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Modeling Document-Level Context for Event Detection via Important Context Selection
Amir Pouran Ben Veyseh | Minh Van Nguyen | Nghia Ngo Trung | Bonan Min | Thien Huu Nguyen
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., BERT) as a critical component in state-of-the-art models for ED. However, the length limit for input texts is a barrier for such ED models as they cannot encode long-range document-level context that has been shown to be beneficial for ED. To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence. The target sentence will be then augmented with the selected sentences and consumed entirely by transformer-based language models for improved representation learning for ED. To this end, the REINFORCE algorithm is employed to train the relevant sentence selection for ED. Several information types are then introduced to form the reward function for the training process, including ED performance, sentence similarity, and discourse relations. Our extensive experiments on multiple benchmark datasets reveal the effectiveness of the proposed model, leading to new state-of-the-art performance.

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Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments
Minh Van Nguyen | Tuan Ngo Nguyen | Bonan Min | Thien Huu Nguyen
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data. An approach to overcome this issue is to use unlabeled data in the target language to aid the alignment of crosslingual representations, i.e., via fooling a language discriminator. However, as this approach does not condition on class information, a target language example of a class could be incorrectly aligned to a source language example of a different class. To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning. In particular, we propose to learn two versions of representation vectors for each class in an REE task based on either source or target language examples. Representation vectors for corresponding classes will then be aligned to achieve class-aware alignment for crosslingual representations. In addition, we propose to further align representation vectors for language-universal word categories (i.e., parts of speech and dependency relations). As such, a novel filtering mechanism is presented to facilitate the learning of word category representations from contextualized representations on input texts based on adversarial learning. We conduct extensive crosslingual experiments with English, Chinese, and Arabic over REE tasks. The results demonstrate the benefits of the proposed method that significantly advances the state-of-the-art performance in these settings.

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ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19
Bonan Min | Benjamin Rozonoyer | Haoling Qiu | Alexander Zamanian | Nianwen Xue | Jessica MacBride
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID-19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. A demonstration video is available at

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Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution
Bonan Min
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference

In this paper, we develop bilingual transfer learning approaches to improve Arabic coreference resolution by leveraging additional English annotation via bilingual or multilingual pre-trained transformers. We show that bilingual transfer learning improves the strong transformer-based neural coreference models by 2-4 F1. We also systemically investigate the effectiveness of several pre-trained transformer models that differ in training corpora, languages covered, and model capacity. Our best model achieves a new state-of-the-art performance of 64.55 F1 on the Arabic OntoNotes dataset. Our code is publicly available at

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Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets
Hemanth Kandula | Bonan Min
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at

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Factuality Assessment as Modal Dependency Parsing
Jiarui Yao | Haoling Qiu | Jin Zhao | Bonan Min | Nianwen Xue
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive. A critical step towards this goal is to determine the factuality of events in text. In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events. We crowdsource the first large-scale data set annotated with modal dependency structures that consists of 353 Covid-19 related news articles, 24,016 events, and 2,938 conceivers. We also develop the first modal dependency parser that jointly extracts events, conceivers and constructs the modal dependency structure of a text. We evaluate the joint model against a pipeline model and demonstrate the advantage of the joint model in conceiver extraction and modal dependency structure construction when events and conceivers are automatically extracted. We believe the dataset and the models will be a valuable resource for a whole host of NLP applications such as fact checking and rumor detection.


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Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach
Bonan Min | Yee Seng Chan | Lingjun Zhao
Proceedings of the Twelfth Language Resources and Evaluation Conference

Automatically analyzing events in a large amount of text is crucial for situation awareness and decision making. Previous approaches treat event extraction as “one size fits all” with an ontology defined a priori. The resulted extraction models are built just for extracting those types in the ontology. These approaches cannot be easily adapted to new event types nor new domains of interest. To accommodate personalized event-centric information needs, this paper introduces the few-shot Event Mention Retrieval (EMR) task: given a user-supplied query consisting of a handful of event mentions, return relevant event mentions found in a corpus. This formulation enables “query by example”, which drastically lowers the bar of specifying event-centric information needs. The retrieval setting also enables fuzzy search. We present an evaluation framework leveraging existing event datasets such as ACE. We also develop a Siamese Network approach, and show that it performs better than ad-hoc retrieval models in the few-shot EMR setting.

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Concept Wikification for COVID-19
Panagiotis Lymperopoulos | Haoling Qiu | Bonan Min
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at

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Weakly Supervised Subevent Knowledge Acquisition
Wenlin Yao | Zeyu Dai | Maitreyi Ramaswamy | Bonan Min | Ruihong Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Subevents elaborate an event and widely exist in event descriptions. Subevent knowledge is useful for discourse analysis and event-centric applications. Acknowledging the scarcity of subevent knowledge, we propose a weakly supervised approach to extract subevent relation tuples from text and build the first large scale subevent knowledge base. We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents. Then, we collect rich weak supervision using the initial seed subevent pairs to train a contextual classifier using BERT and apply the classifier to identify new subevent pairs. The evaluation showed that the acquired subevent tuples (239K) are of high quality (90.1% accuracy) and cover a wide range of event types. The acquired subevent knowledge has been shown useful for discourse analysis and identifying a range of event-event relations.

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Annotating Temporal Dependency Graphs via Crowdsourcing
Jiarui Yao | Haoling Qiu | Bonan Min | Nianwen Xue
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available.

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Exploring Contextualized Neural Language Models for Temporal Dependency Parsing
Hayley Ross | Jonathon Cai | Bonan Min
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering. It is a challenging problem which requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019). In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a). We also present a detailed analysis on why deep contextualized neural LMs help and where they may fall short. Source code and resources are made available at


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Rapid Customization for Event Extraction
Yee Seng Chan | Joshua Fasching | Haoling Qiu | Bonan Min
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Extracting events in the form of who is involved in what at when and where from text, is one of the core information extraction tasks that has many applications such as web search and question answering. We present a system for rapidly customizing event extraction capability to find new event types (what happened) and their arguments (who, when, and where). To enable extracting events of new types, we develop a novel approach to allow a user to find, expand and filter event triggers by exploring an unannotated development corpus. The system will then generate mention level event annotation automatically and train a neural network model for finding the corresponding events. To enable extracting arguments for new event types, the system makes novel use of the ACE annotation dataset to train a generic argument attachment model for extracting Actor, Place, and Time. We demonstrate that with less than 10 minutes of human effort per event type, the system achieves good performance for 67 novel event types. Experiments also show that the generic argument attachment model performs well on the novel event types. Our system (code, UI, documentation, demonstration video) is released as open source.

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Measure Country-Level Socio-Economic Indicators with Streaming News: An Empirical Study
Bonan Min | Xiaoxi Zhao
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Socio-economic conditions are difficult to measure. For example, the U.S. Bureau of Labor Statistics needs to conduct large-scale household surveys regularly to track the unemployment rate, an indicator widely used by economists and policymakers. We argue that events reported in streaming news can be used as “micro-sensors” for measuring socio-economic conditions. Similar to collecting surveys and then counting answers, it is possible to measure a socio-economic indicator by counting related events. In this paper, we propose Event-Centric Indicator Measure (ECIM), a novel approach to measure socio-economic indicators with events. We empirically demonstrate strong correlation between ECIM values to several representative indicators in socio-economic research.

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Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature
Bonan Min | Yee Seng Chan | Haoling Qiu | Joshua Fasching
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences. Towards achieving this grand goal, a crucial first step is to extract past interventions and when and where they have been applied, from hundreds of thousands of reports automatically. In this paper, we developed a corpus annotated with interventions to foster research, and developed an information extraction system for extracting interventions and their location and time from text. We demonstrate early, very encouraging results on extracting interventions.


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A Case Study on Learning a Unified Encoder of Relations
Lisheng Fu | Bonan Min | Thien Huu Nguyen | Ralph Grishman
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.

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When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation
Bonan Min | Marjorie Freedman | Roger Bock | Ralph Weischedel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge
Bonan Min | Marjorie Freedman | Talya Meltzer
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into sub-problems such as mention and named entity recognition, entity linking and relation extraction. However, combining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multiple birthdates). The errors are difficult to resolve solely with IE tools but become obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popularity of entities follow the power-law distribution favoring flat long tails with low-frequency instances. We present a probabilistic joint inference algorithm to incorporate this world knowledge during KB construction. Our approach yields state-of-the-art performance on the TAC Cold Start task, and 42% and 19.4% relative improvements in F1 over our baseline on Cold Start hop-1 and all-hop queries respectively.

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Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks
Bonan Min | Zhuolin Jiang | Marjorie Freedman | Ralph Weischedel
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Typically, relation extraction models are trained to extract instances of a relation ontology using only training data from a single language. However, the concepts represented by the relation ontology (e.g. ResidesIn, EmployeeOf) are language independent. The numbers of annotated examples available for a given ontology vary between languages. For example, there are far fewer annotated examples in Spanish and Japanese than English and Chinese. Furthermore, using only language-specific training data results in the need to manually annotate equivalently large amounts of training for each new language a system encounters. We propose a deep neural network to learn transferable, discriminative bilingual representation. Experiments on the ACE 2005 multilingual training corpus demonstrate that the joint training process results in significant improvement in relation classification performance over the monolingual counterparts. The learnt representation is discriminative and transferable between languages. When using 10% (25K English words, or 30K Chinese characters) of the training data, our approach results in doubling F1 compared to a monolingual baseline. We achieve comparable performance to the monolingual system trained with 250K English words (or 300K Chinese characters) With 50% of training data.

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Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
Lisheng Fu | Thien Huu Nguyen | Bonan Min | Ralph Grishman
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor’s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domain-specific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.


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Infusion of Labeled Data into Distant Supervision for Relation Extraction
Maria Pershina | Bonan Min | Wei Xu | Ralph Grishman
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Distant Supervision for Relation Extraction with an Incomplete Knowledge Base
Bonan Min | Ralph Grishman | Li Wan | Chang Wang | David Gondek
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Challenges in the Knowledge Base Population Slot Filling Task
Bonan Min | Ralph Grishman
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The Knowledge Based Population (KBP) evaluation track of the Text Analysis Conferences (TAC) has been held for the past 3 years. One of the two tasks of KBP is slot filling: finding within a large corpus the values of a set of attributes of given people and organizations. This task has proven very challenging, with top systems rarely exceeding 30% F-measure. In this paper, we present an error analysis and classification for those answers which could be found by a manual corpus search but were not found by any of the systems participating in the 2010 evaluation. The most common sources of failure were limitations on inference, errors in coreference (particularly with nominal anaphors), and errors in named entity recognition. We relate the types of errors to the characteristics of the task and show the wide diversity of problems that must be addressed to improve overall performance.

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Ensemble Semantics for Large-scale Unsupervised Relation Extraction
Bonan Min | Shuming Shi | Ralph Grishman | Chin-Yew Lin
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Compensating for Annotation Errors in Training a Relation Extractor
Bonan Min | Ralph Grishman
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics


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Fine-grained Entity Set Refinement with User Feedback
Bonan Min | Ralph Grishman
Proceedings of the RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition