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Register a new userAdapting Coreference Resolution for Processing Violent Death Narratives
تكييف دقة Aquerence لمعالجة روايات الموت العنيفة
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دقة Aqueference هي مؤسسة كوم النظرية مهمة مهمة في تحليل النص السردي من البيانات السردية من البيانات الإدارية (على سبيل المثال، المصادر السريرية أو الشرطة). ومع ذلك، فإن نماذج Coulsference الحالية Transedon اللغة اللغوية تعاني من قدرة سيئة بسبب ثغرات المجال، ولا سيما عند تطبيقها على النوع الاجتماعي شاملة Datawith Lesbian، مثلي الجنس، المخنثين، والجمنانية الهوائية (LGBT) الفرد. في هذه الورقة، نحن Alyzed تحديات طراز Couress Construcation في شكل مثالي من الإدارة المكتوب باللغة الإنجليزية: الموت العنيف NAR-STATIVS من مراكز الولايات المتحدة الأمريكية DISEASECONTROL (CDC) الوطنية للعنف المنعطف. قمنا بتطوير مجموعة من قواعد البيانات لتحسين طراز Perfor-Mance باستخدام برنامج Probabilistic DataPrampramework. تجارب على روايات قاعدة البيانات الإدارية المقدمة، بالإضافة إلى مجموعات البيانات الأساسية التي تعمل بنظام الأساس، وستدير شيطان فعالية نماذج COMENTION التدريبية لتعزيز البيانات التي يمكن أن تقدم بيانات نصية حول الأفراد LGBT.
Coreference resolution is an important compo-nent in analyzing narrative text from admin-istrative data (e.g., clinical or police sources).However, existing coreference models trainedon general language corpora suffer from poortransferability due to domain gaps, especiallywhen they are applied to gender-inclusive datawith lesbian, gay, bisexual, and transgender(LGBT) individuals.In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA's Centers for DiseaseControl's (CDC) National Violent Death Re-porting System. We developed a set of dataaugmentation rules to improve model perfor-mance using a probabilistic data programmingframework. Experiments on narratives froman administrative database, as well as existinggender-inclusive coreference datasets, demon-strate the effectiveness of data augmentationin training coreference models that can betterhandle text data about LGBT individuals.
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External syntactic and semantic information has been largely ignored by existing neural coreference resolution models. In this paper, we present a heterogeneous graph-based model to incorporate syntactic and semantic structures of sentences. The prop
osed graph contains a syntactic sub-graph where tokens are connected based on a dependency tree, and a semantic sub-graph that contains arguments and predicates as nodes and semantic role labels as edges. By applying a graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experiments on the OntoNotes 5.0 benchmark show the effectiveness of our proposed model.
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In this paper, we present coreference resolution experiments with a newly created multilingual corpus CorefUD (Nedoluzhko et al.,2021). We focus on the following languages: Czech, Russian, Polish, German, Spanish, and Catalan. In addition to monoling
ual experiments, we combine the training data in multilingual experiments and train two joined models - for Slavic languages and for all the languages together. We rely on an end-to-end deep learning model that we slightly adapted for the CorefUD corpus. Our results show that we can profit from harmonized annotations, and using joined models helps significantly for the languages with smaller training data.
Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consis
t mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.
We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages. We introduce the first baseline model for
this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model. We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR. In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators. Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.
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