75 papers with code • 7 benchmarks • 13 datasets
The goal of Slot Filling is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.
Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
Ranked #1 on Question Answering on Natural Questions (long)
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
Ranked #6 on Relation Extraction on Re-TACRED