Challenge: Existing approaches to spotting subjectivity require language-specific tools.
Approach: They develop annotation guidelines for sentence-level subjectivity detection that are not limited to language-specific cues.
Outcome: The proposed framework enables subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation.

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Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings (P18-1)

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Challenge: Existing word embedding methods for natural language processing are limited in their ability to produce dense word embeds.
Approach: They propose a word embedding SentiVec which is infused with sentiment information from a lexical resource and outperforms baselines on subjectivity-sensitive tasks.
Outcome: The proposed word embedding SentiVec outperforms baselines on subjectivity-sensitive tasks.
Manovaad: A Novel Approach to Event Oriented Corpus Creation Capturing Subjectivity and Focus (2020.lrec-1)

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Challenge: Several studies conducted on the different styles of reporting in journalism are essential in understanding phenomena such as media bias and multiple interpretations of the same event.
Approach: They propose a novel method of event reporting that correlates the degree of subjectivity with the geographical closeness of reporting using a Bi-RNN model.
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SubjQA: A Dataset for Subjectivity and Review Comprehension (2020.emnlp-main)

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Challenge: Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified.
Approach: They develop a dataset which investigates subjectivity in question answering . they find that subjectivity is an important feature in the case of QA .
Outcome: The proposed dataset shows that subjectivity is an important feature in question answering (QA) it also shows that subjective questions and answers can have more complex interactions than previously thought.
AMR Beyond the Sentence: the Multi-sentence AMR corpus (C18-1)

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Challenge: Abstract Meaning Representation (AMR) is limited to capturing the semantics of individual sentences.
Approach: They propose a corpus that annotates coreference and similar phenomena on top of existing AMRs.
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SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles (2021.acl-short)

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Challenge: a corpus for satire detection in Romanian news is based on satirical reporting . the goal is to ridicule public figures, politics or contemporary events .
Approach: They propose a corpus for satire detection in Romanian news . they gather 55,608 public news articles from multiple real and satirical sources .
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Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence (2021.findings-acl)

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Challenge: a new corpus-level evaluation approach for event extraction is needed in social science applications . human annotations are often required to extract the actions of political actors and actors . a novel corpus evaluation approach can guide creation of similar social science-oriented resources .
Approach: They propose a corpus-based approach to event extraction that integrates corpus evaluation with real-world social science . they use human annotations to read and label every document for mentions of police activity events .
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BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)

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Challenge: a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles .
Approach: They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection.
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Computing with Subjectivity Lexicons (2020.lrec-1)

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Challenge: a new set of lexicons for expressing subjectivity in text documents is presented . lexiconics are useful resources for identifying semantics relevant to sentiment, emotion, personality, language bias, mood, and attitude.
Approach: They propose a set of lexicons for expressing subjectivity in Brazilian Portuguese text documents . they use word embedding techniques to capture semantically related words to the ones in the lexicos .
Outcome: The proposed lexicons represent different subjectivity dimensions and are more compact in number of terms.
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception (2020.lrec-1)

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Challenge: Fewer studies address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets.
Approach: They propose to annotate 5000 English news headlines with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, and the reader’s perception of the emotion of the headline.
Outcome: The proposed method enables further research on emotion classification, emotion intensity prediction, emotion cause detection and supports qualitative studies.
No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks (2023.findings-emnlp)

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Challenge: a new sentence-level attack on toxic detection models is shown to work on seven languages . toxicity detection systems are used to silence the voices of criticism, causing echo chambers .
Approach: They propose a sentence-level attack that adds positive words to a hateful message . they show the attack works on seven languages from three different language families .
Outcome: The proposed attack is shown to work on seven languages from three different language families.

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