| 1 |
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Open Question Answering with Weakly Supervised Embedding Models ...
|
|
|
|
Abstract:
Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://arxiv.org/abs/1404.4326
https://dx.doi.org/10.48550/arxiv.1404.4326
|
|
BASE
|
|
Hide details
|
|
|
|
|
