6,018 research outputs found
Spoken Language Intent Detection using Confusion2Vec
Decoding speaker's intent is a crucial part of spoken language understanding
(SLU). The presence of noise or errors in the text transcriptions, in real life
scenarios make the task more challenging. In this paper, we address the spoken
language intent detection under noisy conditions imposed by automatic speech
recognition (ASR) systems. We propose to employ confusion2vec word feature
representation to compensate for the errors made by ASR and to increase the
robustness of the SLU system. The confusion2vec, motivated from human speech
production and perception, models acoustic relationships between words in
addition to the semantic and syntactic relations of words in human language. We
hypothesize that ASR often makes errors relating to acoustically similar words,
and the confusion2vec with inherent model of acoustic relationships between
words is able to compensate for the errors. We demonstrate through experiments
on the ATIS benchmark dataset, the robustness of the proposed model to achieve
state-of-the-art results under noisy ASR conditions. Our system reduces
classification error rate (CER) by 20.84% and improves robustness by 37.48%
(lower CER degradation) relative to the previous state-of-the-art going from
clean to noisy transcripts. Improvements are also demonstrated when training
the intent detection models on noisy transcripts
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces well-structured system responses by jointly learning
belief tracking and KB result processing conditioning on the dialog history. We
evaluate the model in a restaurant search domain using a dataset that is
converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
Experiment results show that the proposed model can robustly track dialog state
given the dialog history. Moreover, our model demonstrates promising results in
producing appropriate system responses, outperforming prior end-to-end
trainable neural network models using per-response accuracy evaluation metrics.Comment: Published at Interspeech 201
Sequential Dialogue Context Modeling for Spoken Language Understanding
Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In this paper, we explore novel approaches for modeling
dialogue context in a recurrent neural network (RNN) based language
understanding system. We propose the Sequential Dialogue Encoder Network, that
allows encoding context from the dialogue history in chronological order. We
compare the performance of our proposed architecture with two context models,
one that uses just the previous turn context and another that encodes dialogue
context in a memory network, but loses the order of utterances in the dialogue
history. Experiments with a multi-domain dialogue dataset demonstrate that the
proposed architecture results in reduced semantic frame error rates.Comment: 8 + 2 pages, Updated 10/17: Updated typos in abstract, Updated 07/07:
Updated Title, abstract and few minor change
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