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Prosody-based automatic segmentation of speech into sentences and topics
TLDR
We investigate the use of prosody (information gleaned from the timing and melody of speech) for speech segmentation tasks, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Expand
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Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM
TLDR
This paper proposes a holistic multi-domain, multi-task (i.e. slot filling, domain and intent detection) modeling approach to estimate complete semantic frames for all user utterances addressed to a conversational system, demonstrating the distinctive power of deep learning methods, namely bi-directional recurrent neural network (RNN) with long-short term memory (LSTM) cells to handle such complexity. Expand
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Building a Conversational Agent Overnight with Dialogue Self-Play
TLDR
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. Expand
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Statistical Morphological Disambiguation for Agglutinative Languages
TLDR
We present statistical models for morphological disambiguation in agglutinative languages, with a specific application to Turkish. Expand
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Optimizing SVMs for complex call classification
TLDR
We propose a global optimization process based on an optimal channel communication model that allows a combination of possibly heterogeneous binary classifiers. Expand
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Joint semantic utterance classification and slot filling with recursive neural networks
TLDR
In recent years, continuous space models have proven to be highly effective at language processing tasks ranging from paraphrase detection to language modeling. Expand
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End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding
TLDR
An architecture using end-to-end memory networks to model knowledge carryover in multi-turn conversations, where utterances encoded with intents and slots can be stored as embeddings in the memory and the decoding phase applies an attention model to leverage previously stored semantics for intent prediction and slot tagging simultaneously. Expand
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A statistical information extraction system for Turkish
TLDR
This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods using both the lexical and morphological information. Expand
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What is left to be understood in ATIS?
TLDR
In this paper, our goal is not experimenting with domain specific techniques or features which can help with the remaining SLU errors, but instead exploring methods to realize this utility via extensive error analysis. Expand
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Use of kernel deep convex networks and end-to-end learning for spoken language understanding
TLDR
We present our recent and ongoing work on applying deep learning techniques to spoken language understanding (SLU) problems. Expand
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