9 research outputs found
Query-Based Keyphrase Extraction from Long Documents
Transformer-based architectures in natural language processing force input
size limits that can be problematic when long documents need to be processed.
This paper overcomes this issue for keyphrase extraction by chunking the long
documents while keeping a global context as a query defining the topic for
which relevant keyphrases should be extracted. The developed system employs a
pre-trained BERT model and adapts it to estimate the probability that a given
text span forms a keyphrase. We experimented using various context sizes on two
popular datasets, Inspec and SemEval, and a large novel dataset. The presented
results show that a shorter context with a query overcomes a longer one without
the query on long documents
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and return natural language answers. The aim of the competition was to build systems that can predict correct answers while also satisfying strict on-disk memory budgets. These memory budgets were designed to encourage contestants to explore the trade-off between storing retrieval corpora or the parameters of learned models. In this report, we describe the motivation and organization of the competition, review the best submissions, and analyze system predictions to inform a discussion of evaluation for open-domain QA