Retrieving and reading: A comprehensive survey on open-domain question answering
F Zhu, W Lei, C Wang, J Zheng, S Poria… - arXiv preprint arXiv …, 2021 - arxiv.org
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 …
A survey on deep learning event extraction: Approaches and applications
Q Li, J Li, J Sheng, S Cui, J Wu, Y Hei… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …
from massive textual data. With the rapid development of deep learning, EE based on deep …
An overview of temporal commonsense reasoning and acquisition
G Wenzel, A Jatowt - arXiv preprint arXiv:2308.00002, 2023 - arxiv.org
Temporal commonsense reasoning refers to the ability to understand the typical temporal
context of phrases, actions, and events, and use it to reason over problems requiring such …
context of phrases, actions, and events, and use it to reason over problems requiring such …
Multi-hop question answering
V Mavi, A Jangra, A Jatowt - Foundations and Trends® in …, 2024 - nowpublishers.com
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
V Mavi, A Jangra, A Jatowt - arXiv preprint arXiv:2204.09140, 2022 - arxiv.org
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
J Wang, A Jatowt, M Yoshikawa - … of the 45th International ACM SIGIR …, 2022 - dl.acm.org
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 …
Taxonomy-aware learning for few-shot event detection
J Zheng, F Cai, W Chen, W Lei, H Chen - Proceedings of the Web …, 2021 - dl.acm.org
Event detection classifies unlabeled sentences into event labels, which can benefit
numerous applications, including information retrieval, question answering and script …
numerous applications, including information retrieval, question answering and script …
Event occurrence date estimation based on multivariate time series analysis over temporal document collections
J Wang, A Jatowt, M Yoshikawa - … of the 44th international ACM SIGIR …, 2021 - dl.acm.org
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 …
Improving question answering for event-focused questions in temporal collections of news articles
J Wang, A Jatowt, M Färber, M Yoshikawa - Information Retrieval Journal, 2021 - Springer
Temporal collections of news articles (or news archives) contain numerous accurate and
time-aligned articles, which offer immense value to our society, helping users to know details …
time-aligned articles, which offer immense value to our society, helping users to know details …
Bitimebert: Extending pre-trained language representations with bi-temporal information
J Wang, A Jatowt, M Yoshikawa, Y Cai - Proceedings of the 46th …, 2023 - dl.acm.org
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 …