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Found 16 papers, 2 papers with code
Disentangling Document Topic and Author Gender in Multiple Languages: Lessons for Adversarial Debiasing
Text classification is a central tool in NLP.
Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification
The analysis of public debates crucially requires the classification of political demands according to hierarchical claim ontologies (e. g. for immigration, a supercategory “Controlling Migration” might have subcategories “Asylum limit” or “Border installations”).
Swimming with the Tide? Positional Claim Detection across Political Text Types
Manifestos are official documents of political parties, providing a comprehensive topical overview of the electoral programs.
New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain
Reliable tagging of Temporal Expressions (TEs, e. g., Book a table at L’Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs).
Word-order typology in Multilingual BERT: A case study in subordinate-clause detection
The capabilities and limitations of BERT and similar models are still unclear when it comes to learning syntactic abstractions, in particular across languages.
Meta Learning for Code Summarization
Source code summarization is the task of generating a high-level natural language description for a segment of programming language code.
Between welcome culture and border fence. A dataset on the European refugee crisis in German newspaper reports
Besides the released resources and the case-study, our contribution is also methodological: we talk the reader through the steps from a newspaper article to a discourse network, demonstrating that there is not just one discourse network for the German migration debate, but multiple ones, depending on the topic of interest (political actors, policy fields, time spans).
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language
The recognition of hate speech and offensive language (HOF) is commonly formulated as a classification task to decide if a text contains HOF.
Constraining Linear-chain CRFs to Regular Languages
However, the CRF's Markov assumption makes it impossible for CRFs to represent distributions with \textit{nonlocal} dependencies, and standard CRFs are unable to respect nonlocal constraints of the data (such as global arity constraints on output labels).
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Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled
When humans judge the affective content of texts, they also implicitly assess the correctness of such judgment, that is, their confidence.
Dissecting Span Identification Tasks with Performance Prediction
Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text.
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
Sentiment analysis has a range of corpora available across multiple languages.
Leveraging Lexical Substitutes for Unsupervised Word Sense Induction
Word sense induction is the most prominent unsupervised approach to lexical disambiguation.
Living a discrete life in a continuous world: Reference with distributed representations
Reference is a crucial property of language that allows us to connect linguistic expressions to the world.
"Show me the cup": Reference with Continuous Representations
One of the most basic functions of language is to refer to objects in a shared scene.
