To understand a story with multiple events, it is important to capture the proper relations across these events.
Given a succinct natural language goal, e. g., "make a shelf", and a video of the user's progress so far, the aim of VPA is to devise a plan, i. e., a sequence of actions such as "sand shelf", "paint shelf", etc.
The best version of SEAL that uses NCE ranking method achieves close to +2. 85, +2. 23 respective F1 point gain in average over cross-entropy and INFNET on the feature-based datasets, excluding one outlier that has an excessive gain of +50. 0 F1 points.
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations.
In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective.
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors.
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
Box Embeddings [Vilnis et al., 2018, Li et al., 2019] represent concepts with hyperrectangles in $n$-dimensional space and are shown to be capable of modeling tree-like structures efficiently by training on a large subset of the transitive closure of the WordNet hypernym graph.