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RethinkCWS: Is Chinese Word Segmentation a Solved Task?
The performance of the Chinese Word Segmentation (CWS) systems has gradu...
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Interpretable Multi-dataset Evaluation for Named Entity Recognition
With the proliferation of models for natural language processing tasks, ...
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Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics
In text summarization, evaluating the efficacy of automatic metrics with...
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GSum: A General Framework for Guided Neural Abstractive Summarization
Neural abstractive summarization models are flexible and can produce coh...
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Re-evaluating Evaluation in Text Summarization
Automated evaluation metrics as a stand-in for manual evaluation are an ...
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CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
Neural network-based models augmented with unsupervised pre-trained know...
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Heterogeneous Graph Neural Networks for Extractive Document Summarization
As a crucial step in extractive document summarization, learning cross-s...
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Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis
Microbial communities analysis is drawing growing attention due to the r...
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Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neur...
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Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study
While neural network-based models have achieved impressive performance o...
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RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
In this work, we propose an efficient and accurate monocular 3D detectio...
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Multi-Scale Self-Attention for Text Classification
In this paper, we introduce the prior knowledge, multi-scale structure, ...
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Learning Sparse Sharing Architectures for Multiple Tasks
Most existing deep multi-task learning models are based on parameter sha...
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A Closer Look at Data Bias in Neural Extractive Summarization Models
In this paper, we take stock of the current state of summarization datas...
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Exploring Domain Shift in Extractive Text Summarization
Although domain shift has been well explored in many NLP applications, i...
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Zero-shot Text-to-SQL Learning with Auxiliary Task
Recent years have seen great success in the use of neural seq2seq models...
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DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks
Variants dropout methods have been designed for the fully-connected laye...
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Searching for Effective Neural Extractive Summarization: What Works and What's Next
The recent years have seen remarkable success in the use of deep neural ...
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TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Text infilling is defined as a task for filling in the missing part of a...
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A Combinatorial Algorithm for the Multi-commodity Flow Problem
This paper researches combinatorial algorithms for the multi-commodity f...
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Star-Transformer
Although the fully-connected attention-based model Transformer has achie...
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Drug cell line interaction prediction
Understanding the phenotypic drug response on cancer cell lines plays a ...
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Multi-task Learning over Graph Structures
We present two architectures for multi-task learning with neural sequenc...
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Contextualized Non-local Neural Networks for Sequence Learning
Recently, a large number of neural mechanisms and models have been propo...
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Meta-Learning Multi-task Communication
In this paper, we describe a general framework: Parameters Read-Write Ne...
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Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network
Effective representation of a text is critical for various natural langu...
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Meta Multi-Task Learning for Sequence Modeling
Semantic composition functions have been playing a pivotal role in neura...
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Dynamic Compositional Neural Networks over Tree Structure
Tree-structured neural networks have proven to be effective in learning ...
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Adversarial Multi-task Learning for Text Classification
Neural network models have shown their promising opportunities for multi...
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Syntax-based Attention Model for Natural Language Inference
Introducing attentional mechanism in neural network is a powerful concep...
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Modelling Interaction of Sentence Pair with coupled-LSTMs
Recently, there is rising interest in modelling the interactions of two ...
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Recurrent Neural Network for Text Classification with Multi-Task Learning
Neural network based methods have obtained great progress on a variety o...
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