ﻻ يوجد ملخص باللغة العربية
Previous work in the context of natural language querying of temporal databases has established a method to map automatically from a large subset of English time-related questions to suitable expressions of a temporal logic-like language, called TOP. An algorithm to translate from TOP to the TSQL2 temporal database language has also been defined. This paper shows how TOP expressions could be translated into a simpler logic-like language, called BOT. BOT is very close to traditional first-order predicate logic (FOPL), and hence existing methods to manipulate FOPL expressions can be exploited to interface to time-sensitive applications other than TSQL2 databases, maintaining the existing English-to-TOP mapping.
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises of how the representations and decision rules they learn compare to the ones in humans. In this work, we study representati
Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variab
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All
In this paper, we address the task of spoken language understanding. We present a method for translating spoken sentences from one language into spoken sentences in another language. Given spectrogram-spectrogram pairs, our model can be trained compl
Neural networks models for NLP are typically implemented without the explicit encoding of language rules and yet they are able to break one performance record after another. This has generated a lot of research interest in interpreting the representa