2,988 research outputs found
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201
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
Argument Mining with Structured SVMs and RNNs
We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentative relation prediction. Moreover, our model
supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
transitivity), and can express dependencies between adjacent relations and
propositions. Our approaches outperform unstructured baselines in both web
comments and argumentative essay datasets.Comment: Accepted for publication at ACL 2017. 11 pages, 5 figures. Code at
https://github.com/vene/marseille and data at http://joonsuk.org
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