Do Convolutional Networks need to be Deep for Text Classification ?

We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters. However, a simple shallow-and-wide network outperforms deep models such as DenseNet with word inputs. Our shallow word model further establishes new state-of-the-art performances on two datasets: Yelp Binary (95.9\%) and Yelp Full (64.9\%)

Dialogue Act Recognition Approaches

This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs

How much can Syntax help Sentence Compression ?

International audienceSentence compression involves selecting key information present in the input and rewriting this information into a short, coherent text. Using the Gigaword corpus, we provide a detailed investigation of how syntax can help guide both extractive and abstractive sentence compression. We explore different ways of selecting subtrees from the dependency structure of the input sentence; compare the results of various models and show that preselecting information based on syntax yields promising results

Towards Missing Data Recognition with Cepstral Features

Colloque avec actes et comité de lecture. internationale.International audienceWe study in this work the Missing Data Recognition (MDR) framework applied to a large vocabulary continuous speech recognition (LVCSR) task with cepstral models when the speech signal is corrupted by musical noise. We do not propose a full system that solves this difficult problem, but we rather present some of the issues involved and study some possible solutions to them. We focus in this work on the issues concerning the application of masks to cepstral models. We further identify possible errors and study how some of them affect the performances of the system

Exploiting confidence measures for missing data speech recognition

International audienceAutomatic speech recognition in highly non-stationary noise, for instance with a competing speaker or background music, is an extremely challenging and still unsolved problem. Missing data recognition is a robust approach that is well adapted to this kind of noise. A standard missing data technique consists in marginalizing out, from the observation likelihoods computed during decoding, the contribution of the spectro-temporal fragments that are dominated by noise. However, such an approach can hardly be applied to advanced parameterization domains that do not separate speech from noise frequencies, such as the cepstrum or ETSI AFE. We propose in the work to extend this technique to such parameterization domains, and not only to spectrographic-like front-ends as it was the case before. This is realized by masking the observations that favor erroneous decoding paths, instead of masking the features that are dominated by noise. These new missing data "masks" are now estimated based on speech recognition confidence measures, which can be considered as indicators of the reliability of decoding paths. A first version of this robust algorithm is evaluated on the French broadcast news ESTER corpus

On unsupervised-supervised risk and one-class neural networks

Most unsupervised neural networks training methods concern generative models, deep clustering, pretraining or some form of representation learning. We rather deal in this work with unsupervised training of the final classification stage of a standard deep learning stack, with a focus on two types of methods: unsupervisedsupervised risk approximations and one-class models. We derive a new analytical solution for the former and identify and analyze its similarity with the latter. We apply and validate the proposed approach on multiple experimental conditions, in particular on four challenging recent Natural Language Processing tasks as well as on an anomaly detection task, where it improves over state-of-the-art models

Building and exploiting a dependency treebank for French radio broadcasts

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 31-42. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Unsupervised frame based Semantic Role Induction: application to French and English

International audienceThis paper introduces a novel unsupervised approach to semantic role induction that uses a generative Bayesian model. To the best of our knowledge, it is the first model that jointly clusters syntactic verbs arguments into semantic roles, and also creates verbs classes according to the syntactic frames accepted by the verbs. The model is evaluated on French and English, outperforming, in both cases, a strong baseline. On English, it achieves results comparable to state-of-the-art unsupervised approaches to semantic role induction