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Spoken language understanding (SLU) is a core component of a spoken dialogue system, which involves intent prediction and slot filling and also called semantic frame parsing. Recently recurrent neural networks (RNN) obtained strong results on SLU due to their superior ability of preserving sequential information over time. Traditionally, the SLU component parses semantic frames for utterances considering...
State-of-the-art targeted language understanding systems rely on deep learning methods using 1-hot word vectors or off-the-shelf word embeddings. While word embeddings can be enriched with information from semantic lexicons (such as WordNet and PPDB) to improve their semantic representation, most previous research on word-embedding enriching has focused on improving intrinsic word-level tasks such...
Human-computer interaction and statistical natural language understanding has changed with the addition of a visual display screen in modern mobile devices, as visual rendering is used to communicate the dialog system's response. Onscreen item identification and resolution when interpreting the user utterances is one critical problem to achieve the natural and accurate human-machine communication...
While ensemble models have proven useful for sequence learning tasks there is relatively fewer work that provide insights into what makes them powerful. In this paper, we investigate the empirical behavior of the ensemble approaches on sequence modeling, specifically for the semantic tagging task. We explore this by comparing the performance of commonly used and easy to implement ensemble methods...
Intent detectors in state-of-the-art spoken language understanding systems are often trained with a small number of manually annotated examples collected from the application domain. Search query logs provide a large number of unlabeled queries that would be beneficial to improve such supervised classification. Furthermore, the contents of user queries as well as the clicked URLs provide information...
In this paper, we propose a new framework for semantic template filling in a conversational understanding (CU) system. Our method decomposes the task into two steps: latent n-gram clustering using a semi-supervised latent Dirichlet allocation (LDA) and sequence tagging for learning semantic structures in a CU system. Latent semantic modeling has been investigated to improve many natural language processing...
In natural language human-machine statistical dialog systems, semantic interpretation is a key task typically performed following semantic parsing, and aims to extract canonical meaning representations of semantic components. In the literature, usually manually built rules are used for this task, even for implicitly mentioned non-named semantic components (like genre of a movie or price range of a...
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