Dependency (UML)
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Open
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability …
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Transformer-XL: Attentive Language Models beyond a Fixed-Length Context Open
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond …
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What Does BERT Learn about the Structure of Language? Open
BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks. This result indicates the possibility that BERT networks capture structural information about language. In th…
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Universal Dependencies v1: A Multilingual Treebank Collection Open
Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependenci…
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Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling Open
Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to synt…
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Graph Convolution over Pruned Dependency Trees Improves Relation Extraction Open
Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or…
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SQuAD: 100,000+ Questions for Machine Comprehension of Text Open
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text f…
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Can sub-Saharan Africa feed itself? Open
Significance The question whether sub-Saharan Africa (SSA) can be self-sufficient in cereals by 2050 is of global relevance. Currently, SSA is amongst the (sub)continents with the largest gap between cereal consumption and production, wher…
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A protocol for conducting and presenting results of regression‐type analyses Open
Summary Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, …
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Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction Open
Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi com…
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A Comparative Analysis of Community Detection Algorithms on Artificial Networks Open
Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-wor…
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CamemBERT: a Tasty French Language Model Open
Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical…
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Aspect Based Sentiment Analysis with Gated Convolutional Networks Open
Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches in…
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Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Open
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond …
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Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Open
We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors a…
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Relational Graph Attention Network for Aspect-based Sentiment Analysis Open
Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However…
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A Review of Deep Transfer Learning and Recent Advancements Open
Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two significant constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learnin…
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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting Open
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on ro…
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MPNet: Masked and Permuted Pre-training for Language Understanding Open
BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models. Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to…
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Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks Open
Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency tree are widely applied for aspect-based s…
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Attention Guided Graph Convolutional Networks for Relation Extraction Open
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependen…
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Open
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability …
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An Ontology-based Context Model in Intelligent Environments Open
Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a …
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GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction Open
In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction betwee…
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Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos Open
Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address…
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Cauchy Combination Test: A Powerful Test With Analytic <i>p</i>-Value Calculation Under Arbitrary Dependency Structures Open
Combining individual p-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher's combination test. In modern large-scale data analysis, correlation and sparsity are common fe…
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Named Entity Recognition as Dependency Parsing Open
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the f…
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Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell Lines Open
One of the main goals of the Cancer Dependency Map project is to systematically identify cancer vulnerabilities across cancer types to accelerate therapeutic discovery. Project Achilles serves this goal through the in vitro study of geneti…
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Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree Open
Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).…
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Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction Open
Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting ha…