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Found 24 papers, 4 papers with code
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types.
Task Transfer and Domain Adaptation for Zero-Shot Question Answering
Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks.
Do Answers to Boolean Questions Need Explanations? Yes
Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question.
Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering
Recent approaches have exploited weaknesses in monolingual question answering (QA) models by adding adversarial statements to the passage.
Multilingual Transfer Learning for QA Using Translation as Data Augmentation
Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages.
A Multilingual Reading Comprehension System for more than 100 Languages
We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019).
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
We present the results and main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2020).
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification
The widespread use of offensive content in social media has led to an abundance of research in detecting language such as hate speech, cyberbullying, and cyber-aggression.
SemEval-2013 Task 2: Sentiment Analysis in Twitter
To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask.
SemEval-2014 Task 9: Sentiment Analysis in Twitter
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014.
SemEval-2015 Task 10: Sentiment Analysis in Twitter
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter.
SemEval-2016 Task 4: Sentiment Analysis in Twitter
The three new subtasks focus on two variants of the basic ``sentiment classification in Twitter'' task.
SemEval-2017 Task 4: Sentiment Analysis in Twitter
This paper describes the fifth year of the Sentiment Analysis in Twitter task.
Leveraging Medical Literature for Section Prediction in Electronic Health Records
We propose using sections from medical literature (e. g., textbooks, journals, web content) that contain content similar to that found in EHR sections.
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval).
Predicting the Type and Target of Offensive Posts in Social Media
In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media.
Toward Cross-Domain Engagement Analysis in Medical Notes
We present a novel annotation task evaluating a patient{'}s engagement with their health care regimen.
Annotating Agreement and Disagreement in Threaded Discussion
We introduce a new corpus of sentence-level agreement and disagreement annotations over LiveJournal and Wikipedia threads.
