ymoslem/acl-abstracts huggingface dataset:
A Trigger-Aware Multi-Task Learning for Chinese Event Entity Recognition (2021)

This paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific event type. Currently there is no specific model to directly deal with the EER task. Previous namedentity recognition methods that combine both relation extraction and argument role classification(named NER+TD+ARC) can be adapted for the task by utilizing the relation extraction component for event trigger detection (TD). However these technical alternatives heavily rely on the efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising (2021)

Deep neural networks have achieved state-of-the-art performances on named entity recognition(NER) with sufficient training data while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem we propose a novel data augmentation method based on pre-trained language model (PLM) and curriculum learning strategy. Concretely we use the PLMto generate diverse training instances through predicting different masked words and design atask-specific curriculum learning strategy to alleviate the influence of noises. We evaluate the effectiveness of our approach on three datasets: CoNLL-2003 OntoNotes5.0 and MaScip of which the first two are simulated low-resource scenarios and the last one is a real low-resource dataset in material science domain. Experimental results show that our method consistently outperform the baseline model. Specifically our method achieves an absolute improvement of3.46{\%} F1 score on the 1{\%} CoNLL-2003 2.58{\%} on the 1{\%} OntoNotes5.0 and 0.99{\%} on the full of MaScip.
Global entity alignment with Gated Latent Space Neighborhood Aggregation (2021)

Existing entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However they are still challenged by the heterogeneous topological neighborhood structures which could cause the models to produce different representations of counterpart entities. In the paper we propose a global entity alignment model with gated latent space neighborhood aggregation (LatsEA) to address this challenge. Latent space neighborhood is formed by calculating the similarity between the entity embeddings it can introduce long-range neighbors to expand the topological neighborhood and reconcile the heterogeneous neighborhood structures. Meanwhile it uses vanilla GCN to aggregate the topological neighborhood and latent space neighborhood respectively. Then it uses an average gating mechanism to aggregate topological neighborhood information and latent space neighborhood information of the central entity. In order to further consider the interdependence between entity alignment decisions we propose a global entity alignment strategy i.e. formulate entity alignment as the maximum bipartite matching problem which is effectively solved by Hungarian algorithm. Our experiments with ablation studies on three real-world entity alignment datasets prove the effectiveness of the proposed model. Latent space neighborhood informationand global entity alignment decisions both contributes to the entity alignment performance improvement.
Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation (2021)

Charge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction on high-frequency charges has achieved promising results but that on few-shot chargesis still challenging. In this work we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically we extract coarse- and fine-grained features to enhance the model`s capability on representation based on which the few-shot chargescan be better distinguished. Furthermore we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides our method can work wellon tiny datasets and has better efficiency in the training which provides better applicability in realscenarios.
Sketchy Scene Captioning: Learning Multi-Level Semantic Information from Sparse Visual Scene Cues (2021)

To enrich the research about sketch modality a new task termed Sketchy Scene Captioning isproposed in this paper. This task aims to generate sentence-level and paragraph-level descrip-tions for a sketchy scene. The sentence-level description provides the salient semantics of asketchy scene while the paragraph-level description gives more details about the sketchy scene. Sketchy Scene Captioning can be viewed as an extension of sketch classification which can onlyprovide one class label for a sketch. To generate multi-level descriptions for a sketchy scene ischallenging because of the visual sparsity and ambiguity of the sketch modality. To achieve ourgoal we first contribute a sketchy scene captioning dataset to lay the foundation of this new task. The popular sequence learning scheme e.g. Long Short-Term Memory neural network with vi-sual attention mechanism is then adopted to recognize the objects in a sketchy scene and inferthe relations among the objects. In the experiments promising results have been achieved on the proposed dataset. We believe that this work will motivate further researches on the understanding of sketch modality and the numerous sketch-based applications in our daily life. The collected dataset is released at \url{https://github.com/SketchysceneCaption/Dataset}.
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network (2021)

Building an interpretable AI diagnosis system for breast cancer is an important embodiment ofAI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning areeasy to explain but the accuracy is very low. Deep neural network greatly improves the accuracy of diagnosis but the black box model does not provide transparency and interpretation. In this work we propose a semantic embedding self-explanatory Breast Diagnostic Capsules Network(BDCN). This model is the first to combine the capsule network with semantic embedding for theAI diagnosis of breast tumors using capsules to simulate semantics. We pre-trained the extrac-tion word vector by embedding the semantic tree into the BERT and used the capsule network to improve the semantic representation of multiple heads of attention to construct the extraction feature the capsule network was extended from the computer vision classification task to the text classification task. Simultaneously both the back propagation principle and dynamic routing algorithm are used to realize the local interpretability of the diagnostic model. The experimental results show that this breast diagnosis model improves the model performance and has good interpretability which is more suitable for clinical situations. IntroductionBreast cancer is an important killer threatening women`s health because of rising incidence. Early detection and diagnosis are the key to reduce the mortality rate of breast cancer and improve the quality of life of patients. Mammary gland molybdenum target report contains rich semantic information whichcan directly reflect the results of breast cancer screening (CACA-CBCS 2019) and AI-assisted diagno-sis of breast cancer is an important means. Therefore various diagnostic models were born. Mengwan(2020) used support vector machine(SVM) and Naive Bayes to classify morphological features with anaccuracy of 91.11{\%}. Wei (2009) proposed a classification method of breast cancer based on SVM andthe accuracy of the classifier experiment is 79.25{\%}. These traditional AI diagnoses of breast tumors havelimited data volume and low accuracy. Deep Neural Networks (DNN) enters into the ranks of the diagno-sis of breast tumor. Wang (2019) put forward a kind of based on feature fusion with CNN deep features of breast computer-aided diagnosis methods the accuracy is 92.3{\%}. Zhao (2018) investigated capsule networks with dynamic routing for text classification which proves the feasibility of text categorization. Existing models have poor predictive effect and lack of interpretation which can not meet the clinical needs.
GCN with External Knowledge for Clinical Event Detection (2021)

In recent years with the development of deep learning and the increasing demand for medical information acquisition in medical information technology applications such as clinical decision support Clinical Event Detection has been widely studied as its subtask. However directly applying advances in deep learning to Clinical Event Detection tasks often produces undesirable results. This paper proposes a multi-granularity information fusion encoder-decoder frameworkthat introduces external knowledge. First the word embedding generated by the pre-trained biomedical language representation model (BioBERT) and the character embedding generatedby the Convolutional Neural Network are spliced. And then perform Part-of-Speech attention coding for character-level embedding perform semantic Graph Convolutional Network codingfor the spliced character-word embedding. Finally the information of these three parts is fusedas Conditional Random Field input to generate the sequence label of the word. The experimental results on the 2012 i2b2 data set show that the model in this paper is superior to other existingmodels. In addition the model in this paper alleviates the problem that {\textquotedblleft}occurrence{\textquotedblright} event typeseem more difficult to detect than other event types.
A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing (2021)

With the increasing popularity of learning Chinese as a second language (L2) the development of an automatic essay scoring (AES) method specially for Chinese L2 essays has become animportant task. To build a robust model that could easily adapt to prompt changes we propose 90linguistic features with consideration of both language complexity and correctness and introducethe Ordinal Logistic Regression model that explicitly combines these linguistic features and low-level textual representations. Our model obtains a high QWK of 0.714 a low RMSE of 1.516 anda considerable Pearson correlation of 0.734. With a simple linear model we further analyze the contribution of the linguistic features to score prediction revealing the model`s interpretability and its potential to give writing feedback to users. This work provides insights and establishes asolid baseline for Chinese L2 AES studies.
A Robustly Optimized BERT Pre-training Approach with Post-training (2021)

In the paper we present a {\textquoteleft}pre-training'+{\textquoteleft}post-training'+{\textquoteleft}fine-tuning' three-stage paradigm which is a supplementary framework for the standard {\textquoteleft}pre-training'+{\textquoteleft}fine-tuning' languagemodel approach. Furthermore based on three-stage paradigm we present a language modelnamed PPBERT. Compared with original BERT architecture that is based on the standard two-stage paradigm we do not fine-tune pre-trained model directly but rather post-train it on the domain or task related dataset first which helps to better incorporate task-awareness knowl-edge and domain-awareness knowledge within pre-trained model also from the training datasetreduce bias. Extensive experimental results indicate that proposed model improves the perfor-mance of the baselines on 24 NLP tasks which includes eight GLUE benchmarks eight Su-perGLUE benchmarks six extractive question answering benchmarks. More remarkably our proposed model is a more flexible and pluggable model where post-training approach is able to be plugged into other PLMs that are based on BERT. Extensive ablations further validate the effectiveness and its state-of-the-art (SOTA) performance. The open source code pre-trained models and post-trained models are available publicly.
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report (2021)

This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of socio-political events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.
Keynote Abstract: Events on a Global Scale: Towards Language-Agnostic Event Extraction (2021)

Event extraction is a challenging and exciting task in the world of machine learning {\&} natural language processing. The breadth of events of possible interest, the speed at which surrounding socio-political event contexts evolve, and the complexities involved in generating representative annotated data all contribute to this challenge. One particular dimension of difficulty is the intrinsically global nature of events: many downstream use cases for event extraction involve reporting not just in a few major languages but in a much broader context. The languages of interest for even a fixed task may still shift from day to day, e.g. when a disease emerges in an unexpected location. Early approaches to multi-lingual event extraction (e.g. ACE) relied wholly on supervised data provided in each language of interest. Later approaches leveraged the success of machine translation to side-step the issue, simply translating foreign-language content to English and deploying English models on the result (often leaving some significant portion of the original content behind). Most recently, however, the community has begun to shown significant progress applying zero-shot transfer techniques to the problem, developing models using supervised English data but decoding in a foreign language without translation, typically using embedding spaces specifically designed to capture multi-lingual semantic content. In this talk I will discuss multiple dimensions of these promising new approaches and the linguistic representations that underlie them. I will compare them with approaches based on machine translation (as well as with models trained using in-language training data, where available), and discuss their strengths and weaknesses in different contexts, including the amount of English/foreign bitext available and the nature of the target event ontology. I will also discuss possible future directions with an eye to improving the quality of event extraction no matter its source around the globe.
Keynote Abstract: Machine Learning in Conflict Studies: Reflections on Ethics, Collaboration, and Ongoing Challenges (2021)

Advances in machine learning are nothing short of revolutionary in their potential to analyze massive amounts of data and in doing so, create new knowledge bases. But there is a responsibility in wielding the power to analyze these data since the public attributes a high degree of confidence to results which are based on big datasets. In this keynote, I will first address our ethical imperative as scholars to {\textquotedblleft}get it right.{\textquotedblright} This imperative relates not only to model precision but also to the quality of the underlying data, and to whether the models inadvertently reproduce or obscure political biases in the source material. In considering the ethical imperative to get it right, it is also important to define what is {\textquotedblleft}right{\textquotedblright}: what is considered an acceptable threshold for classification success needs to be understood in light of the project`s objectives. I then reflect on the different topics and data which are sourced in this field. Much of the existing research has focused on identifying conflict events (e.g. battles), but scholars are also increasingly turning to ML approaches to address other facets of the conflict environment. Conflict event extraction has long been a challenge for the natural language processing (NLP) community because it requires sophisticated methods for defining event ontologies, creating language resources, and developing algorithmic approaches. NLP machine-learning tools are ill-adapted to the complex, often messy, and diverse data generated during conflicts. Relative to other types of NLP text corpora, conflicts tend to generate less textual data, and texts are generated non-systematically. Conflict-related texts are often lexically idiosyncratic and tend to be written differently across actors, periods, and conflicts. Event definition and adjudication present tough challenges in the context of conflict corpora. Topics which rely on other types of data may be better-suited to NLP and machine learning methods. For example, Twitter and other social media data lend themselves well to studying hate speech, public opinion, social polarization, or discursive aspects of conflictual environments. Likewise, government-produced policy documents have typically been analyzed with historical, qualitative methods but their standardized formats and quantity suggest that ML methods can provide new traction. ML approaches may also allow scholars to exploit local sources and multi-language sources to a greater degree than has been possible. Many challenges remain, and these are best addressed in collaborative projects which build on interdisciplinary expertise. Classification projects need to be anchored in the theoretical interests of scholars of political violence if the data they produce are to be put to analytical use. There are few ontologies for classification that adequately reflect conflict researchers' interests, which highlights the need for conceptual as well as technical development.
PROTEST-ER: Retraining BERT for Protest Event Extraction (2021)

We analyze the effect of further retraining BERT with different domain specific data as an unsupervised domain adaptation strategy for event extraction. Portability of event extraction models is particularly challenging, with large performance drops affecting data on the same text genres (e.g., news). We present PROTEST-ER, a retrained BERT model for protest event extraction. PROTEST-ER outperforms a corresponding generic BERT on out-of-domain data of 8.1 points. Our best performing models reach 51.91-46.39 F1 across both domains.
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation (2021)

Most of the existing information extraction frameworks (Wadden et al., 2019; Veysehet al., 2020) focus on sentence-level tasks and are hardly able to capture the consolidated information from a given document. In our endeavour to generate precise document-level information frames from lengthy textual records, we introduce the task of Information Aggregation or Argument Aggregation. More specifically, our aim is to filter irrelevant and redundant argument mentions that were extracted at a sentence level and render a document level information frame. Majority of the existing works have been observed to resolve related tasks of document-level event argument extraction (Yang et al., 2018; Zheng et al., 2019) and salient entity identification (Jain et al., 2020) using supervised techniques. To remove dependency from large amounts of labelled data, we explore the task of information aggregation using weakly supervised techniques. In particular, we present an extractive algorithm with multiple sieves which adopts active learning strategies to work efficiently in low-resource settings. For this task, we have annotated our own test dataset comprising of 131 document information frames and have released the code and dataset to further research prospects in this new domain. To the best of our knowledge, we are the first to establish baseline results for this task in English. Our data and code are publicly available at \url{https://github.com/DebanjanaKar/ArgFuse}.
Modality and Negation in Event Extraction (2021)

Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998--2020 (2021)

We apply statistical techniques from natural language processing to a collection of Western and Hong Kong{--}based English-language newspaper articles spanning the years 1998{--}2020, studying the difference and evolution of its portrayal. We observe that both content and attitudes differ between Western and Hong Kong{--}based sources. ANOVA on keyword frequencies reveals that Hong Kong{--}based papers discuss protests and democracy less often. Topic modeling detects salient aspects of protests and shows that Hong Kong{--}based papers made fewer references to police violence during the Anti{--}Extradition Law Amendment Bill Movement. Diachronic shifts in word embedding neighborhoods reveal a shift in the characterization of salient keywords once the Movement emerged. Together, these raise questions about the existence of anodyne reporting from Hong Kong{--}based media. Likewise, they illustrate the importance of sample selection for protest event analysis.