Qa dataset explosion: A taxonomy of nlp resources for question answering and reading comprehension
Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …
there has been much work on benchmark datasets needed to track modeling progress …
Retrieving and reading: A comprehensive survey on open-domain question answering
Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …
Processing (NLP), which aims to answer a question in the form of natural language based …
Multi-hop question answering
Abstract The task of Question Answering (QA) has attracted significant research interest for a
long time. Its relevance to language understanding and knowledge retrieval tasks, along …
long time. Its relevance to language understanding and knowledge retrieval tasks, along …
A survey on multi-hop question answering and generation
The problem of Question Answering (QA) has attracted significant research interest for long.
Its relevance to language understanding and knowledge retrieval tasks, along with the …
Its relevance to language understanding and knowledge retrieval tasks, along with the …
Archivalqa: A large-scale benchmark dataset for open-domain question answering over historical news collections
In the last few years, open-domain question answering (ODQA) has advanced rapidly due to
the development of deep learning techniques and the availability of large-scale QA datasets …
the development of deep learning techniques and the availability of large-scale QA datasets …
Event occurrence date estimation based on multivariate time series analysis over temporal document collections
Real world events are quite often mentioned in texts. Estimating the occurrence time of event
mentions has many applications in IR, QA, general document understanding and …
mentions has many applications in IR, QA, general document understanding and …
Temporal Blind Spots in Large Language Models
Large language models (LLMs) have recently gained significant attention due to their
unparalleled zero-shot performance on various natural language processing tasks …
unparalleled zero-shot performance on various natural language processing tasks …
Bitimebert: Extending pre-trained language representations with bi-temporal information
Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this
work, we investigate methods for incorporating temporal information during pre-training to …
work, we investigate methods for incorporating temporal information during pre-training to …
[PDF][PDF] TimeBERT: Extending pre-trained language representations with temporal information
Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this
work, we investigate methods for incorporating temporal information during pre-training to …
work, we investigate methods for incorporating temporal information during pre-training to …
Machine learning algorithm for text categorization of news articles from Senegalese online news websites
The growth of information in the form of news articles is a big problem in every society. This
information lies in unstructured form and manually managing and effectively making use of it …
information lies in unstructured form and manually managing and effectively making use of it …