The sliding window approach provides an elegant way to handle contexts o...
Instruction fine-tuned language models on a collection of instruction
an...
Many information retrieval tasks require large labeled datasets for
fine...
The field of Question Answering (QA) has made remarkable progress in rec...
Neural information retrieval (IR) systems have progressed rapidly in rec...
Recent machine reading comprehension datasets include extractive and boo...
Machine learning models are prone to overfitting their source (training)...
Transition-based parsers for Abstract Meaning Representation (AMR) rely ...
Neural passage retrieval is a new and promising approach in open retriev...
Relation extraction (RE) is an important information extraction task whi...
Despite extensive research on parsing of English sentences into Abstract...
We propose a transition-based system to transpile Abstract Meaning
Repre...
AMR parsing has experienced an unprecendented increase in performance in...
Existing datasets that contain boolean questions, such as BoolQ and TYDI...
Predicting linearized Abstract Meaning Representation (AMR) graphs using...
Existing models on Machine Reading Comprehension (MRC) require complex m...
Abstract Meaning Representation parsing is a sentence-to-graph predictio...
We develop high performance multilingualAbstract Meaning Representation ...
Prior work on multilingual question answering has mostly focused on usin...
End-to-end question answering (QA) requires both information retrieval (...
Abstract Meaning Representation (AMR) parsing has experienced a notable
...
Modeling the parser state is key to good performance in transition-based...
Relation extraction (RE) is one of the most important tasks in informati...
Transfer learning techniques are particularly useful in NLP tasks where ...
Named Entity Recognition (NER) is an essential precursor task for many
n...
The task of event detection and classification is central to most inform...
Abstract Meaning Representations (AMRs) are broad-coverage sentence-leve...
Information extraction is an important task in NLP, enabling the automat...
We introduce TechQA, a domain-adaptation question answering dataset for ...
Relation extraction (RE) seeks to detect and classify semantic relations...
Many of the top question answering systems today utilize ensembling to
i...
Existing literature on Question Answering (QA) mostly focuses on algorit...
This paper introduces a novel orchestration framework, called CFO
(COMPU...
Our work involves enriching the Stack-LSTM transition-based AMR parser
(...
Multi-hop reading comprehension focuses on one type of factoid question,...
We propose an entity-centric neural cross-lingual coreference model that...
A major challenge in Entity Linking (EL) is making effective use of
cont...
Entity linking (EL) is the task of disambiguating mentions in text by
as...
The state-of-the-art named entity recognition (NER) systems are supervis...
The state-of-the-art named entity recognition (NER) systems are statisti...
Slot Filling (SF) aims to extract the values of certain types of attribu...
This paper describes an application of reinforcement learning to the men...
Natural language sentence matching is a fundamental technology for a var...
Previous machine comprehension (MC) datasets are either too small to tra...
We consider the problem of using sentence compression techniques to
faci...
One of the key challenges in natural language processing (NLP) is to yie...