In multitask retrieval, a single retriever is trained to retrieve releva...
A conventional approach to entity linking is to first find mentions in a...
The choice of negative examples is important in noise contrastive estima...
Biomedical entity linking is the task of identifying mentions of biomedi...
We propose to tackle conditional text generation tasks, especially those...
It is a common belief in the NLP community that continuous bag-of-words
...
Dataless text classification is capable of classifying documents into
pr...
Accurate lexical entailment (LE) and natural language inference (NLI) of...
We seek to improve text classification by leveraging naturally annotated...
While much work on deep latent variable models of text uses continuous l...
We propose learning discrete structured representations from unlabeled d...
Rich entity representations are useful for a wide class of problems invo...
Retrieve-and-edit based approaches to structured prediction, where struc...
This work develops a simple information theoretic framework that capture...
In practice, most spoken language understanding systems process user inp...
Pre-trained word embeddings improve the performance of a neural model at...
We introduce a novel sub-character architecture that exploits a unique
c...
Standard approaches in entity identification hard-code boundary detectio...