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Found 12 papers, 1 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.
Learning to Transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
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.
Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on
Relation Linking
on QALD-9
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).
Exploring Software Naturalness through Neural Language Models
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification
In this work, we combine these two approaches to improve low-shot text classification with two novel methods: a simple bag-of-words embedding approach; and a more complex context-aware method, based on the BERT model.
Stacking With Auxiliary Features for Entity Linking in the Medical Domain
In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System.
Scoring Disease-Medication Associations using Advanced NLP, Machine Learning, and Multiple Content Sources
In predicting positive associations, the stacked combination significantly outperformed the baseline (a distant semi-supervised method on large medical text), achieving F scores of 0. 75 versus 0. 55 on the pairs seen in the patient records, and F scores of 0. 69 and 0. 35 on unique pairs.
