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Register a new userGENE: Global Event Network Embedding
الجينات: شبكة الحدث العالمي تضمين
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الطرق الحالية لتمثيل الأحداث تجاهل الأحداث ذات الصلة في السياق العالمي على مستوى كوربوس.لفهم عميق وشامل للأحداث المعقدة، نقدم مهمة جديدة، وتضمين شبكة الأحداث، والتي تهدف إلى تمثيل الأحداث من خلال التقاط الاتصالات بين الأحداث.نقترح إطارا جديدا، وتضمين شبكة الحدث العالمي (جين)، الذي يرمز شبكة الحدث مع تشفير رسم بياني متعدد المشتريات مع الحفاظ على طوبولوجيا الرسم البياني وعلم العقدة.يتم تدريب تشفير الرسم البياني عن طريق تقليل كل من الخسائر الهيكلية والدلالية.نحن نطور سلسلة جديدة من المهام التحقيق المهيكلية، وإظهار أن نهجنا يفوق بشكل فعال على نماذج خط الأساس على كتابة العقدة، وتصنيف دور الوسيطة، وقضية كور معلومات الحدث.
Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events. We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics. The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution.
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Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying entirely on the contextual se
mantic features of each word in the inputted text, which we find is easy to be confused by the varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (SS-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, SS-JDN effectively exceeds ten recent strong baselines on ACE2005 and KBP2015 datasets. Further, we perform extensive experiments to comprehensively probe SS-JDN.
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Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has long been recognized by communities in natural language gener
ation, especially in text summarization. It remains challenging for a state-of-the-art neural abstractive summarization model to generate a well-integrated summary sentence. In this paper, we explore the effective sentence fusion method in the context of text summarization. We propose to build an event graph from the input sentences to effectively capture and organize related events in a structured way and use the constructed event graph to guide sentence fusion. In addition to make use of the attention over the content of sentences and graph nodes, we further develop a graph flow attention mechanism to control the fusion process via the graph structure. When evaluated on sentence fusion data built from two summarization datasets, CNN/DaliyMail and Multi-News, our model shows to achieve state-of-the-art performance in terms of Rouge and other metrics like fusion rate and faithfulness.
Abstract We present a new conjunctivist framework, neural event semantics (NES), for compositional grounded language understanding. Our approach treats all words as classifiers that compose to form a sentence meaning by multiplying output scores. The
se classifiers apply to spatial regions (events) and NES derives its semantic structure from language by routing events to different classifier argument inputs via soft attention. NES is trainable end-to-end by gradient descent with minimal supervision. We evaluate our method on compositional grounded language tasks in controlled synthetic and real-world settings. NES offers stronger generalization capability than standard function-based compositional frameworks, while improving accuracy over state-of-the-art neural methods on real-world language tasks.
Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models---most recently pre-trained, Transformer language models---have demonstrated improvements in m
odeling event plausibility, their performance still falls short of humans'. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that a person breathing'' is plausible while a dentist breathing'' is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.
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