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Top-down Discourse Parsing via Sequence Labelling
We introduce a top-down approach to discourse parsing that is conceptual...
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Diverse Adversaries for Mitigating Bias in Training
Adversarial learning can learn fairer and less biased models of language...
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FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
In this paper, we propose FFCI, a framework for automatic summarization ...
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Liputan6: A Large-scale Indonesian Dataset for Text Summarization
In this paper, we introduce a large-scale Indonesian summarization datas...
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IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP
Although the Indonesian language is spoken by almost 200 million people ...
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Target Word Masking for Location Metonymy Resolution
Existing metonymy resolution approaches rely on features extracted from ...
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Learning Contextualised Cross-lingual Word Embeddings for Extremely Low-Resource Languages Using Parallel Corpora
We propose a new approach for learning contextualised cross-lingual word...
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COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
We present COVID-SEE, a system for medical literature discovery based on...
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Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?
As part of growing NLP capabilities, coupled with an awareness of the et...
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WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
We present our work on aligning the Unified Medical Language System (UML...
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You are right. I am ALARMED – But by Climate Change Counter Movement
The world is facing the challenge of climate crisis. Despite the consens...
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SemEval-2017 Task 3: Community Question Answering
We describe SemEval-2017 Task 3 on Community Question Answering. This ye...
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Improved Document Modelling with a Neural Discourse Parser
Despite the success of attention-based neural models for natural languag...
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Deep Ordinal Regression for Pledge Specificity Prediction
Many pledges are made in the course of an election campaign, forming imp...
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Evaluating the Utility of Document Embedding Vector Difference for Relation Learning
Recent work has demonstrated that vector offsets obtained by subtracting...
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Semi-supervised Stochastic Multi-Domain Learning using Variational Inference
Supervised models of NLP rely on large collections of text which closely...
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Target Based Speech Act Classification in Political Campaign Text
We study pragmatics in political campaign text, through analysis of spee...
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Contextualization of Morphological Inflection
Critical to natural language generation is the production of correctly i...
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A Joint Model for Multimodal Document Quality Assessment
The quality of a document is affected by various factors, including gram...
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Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme
In this paper, we propose a joint architecture that captures language, r...
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Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
Online petitions are a cost-effective way for citizens to collectively e...
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Narrative Modeling with Memory Chains and Semantic Supervision
Story comprehension requires a deep semantic understanding of the narrat...
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Towards Robust and Privacy-preserving Text Representations
Written text often provides sufficient clues to identify the author, the...
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What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Most real world language problems require learning from heterogenous cor...
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Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis
Election manifestos document the intentions, motives, and views of polit...
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Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis
While neural networks have been shown to achieve impressive results for ...
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Automatic Language Identification in Texts: A Survey
Language identification (LI) is the problem of determining the natural l...
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Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields
Despite successful applications across a broad range of NLP tasks, condi...
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Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
We propose a method for embedding two-dimensional locations in a continu...
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An Automatic Approach for Document-level Topic Model Evaluation
Topic models jointly learn topics and document-level topic distribution....
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Topically Driven Neural Language Model
Language models are typically applied at the sentence level, without acc...
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A Neural Model for User Geolocation and Lexical Dialectology
We propose a simple yet effective text- based user geolocation model bas...
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Context-Aware Prediction of Derivational Word-forms
Derivational morphology is a fundamental and complex characteristic of l...
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Automatic Labelling of Topics with Neural Embeddings
Topics generated by topic models are typically represented as list of te...
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Named Entity Recognition for Novel Types by Transfer Learning
In named entity recognition, we often don't have a large in-domain train...
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An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word...
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From Incremental Meaning to Semantic Unit (phrase by phrase)
This paper describes an experimental approach to Detection of Minimal Se...
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Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Recent work on word embeddings has shown that simple vector subtraction ...
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Twitter User Geolocation Using a Unified Text and Network Prediction Model
We propose a label propagation approach to geolocation prediction based ...
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Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Word embeddings -- distributed word representations that can be learned ...
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