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"speech": models, code, and papers

Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015

Jun 18, 2016
Hassan Sajjad, Nadir Durrani, Francisco Guzman, Preslav Nakov, Ahmed Abdelali, Stephan Vogel, Wael Salloum, Ahmed El Kholy, Nizar Habash

The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres.


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A Tutorial on Deep Neural Networks for Intelligent Systems

Mar 23, 2016
Juan C. Cuevas-Tello, Manuel Valenzuela-Rendon, Juan A. Nolazco-Flores

Developing Intelligent Systems involves artificial intelligence approaches including artificial neural networks. Here, we present a tutorial of Deep Neural Networks (DNNs), and some insights about the origin of the term "deep"; references to deep learning are also given. Restricted Boltzmann Machines, which are the core of DNNs, are discussed in detail. An example of a simple two-layer network, performing unsupervised learning for unlabeled data, is shown. Deep Belief Networks (DBNs), which are used to build networks with more than two layers, are also described. Moreover, examples for supervised learning with DNNs performing simple prediction and classification tasks, are presented and explained. This tutorial includes two intelligent pattern recognition applications: hand- written digits (benchmark known as MNIST) and speech recognition.

* 30 pages, 19 figures, unpublished technical report 

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Deep Networks With Large Output Spaces

Apr 10, 2015
Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, Jay Yagnik

Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, they are still prohibitively expensive to train and apply for problems containing millions of classes in the output layer. Based on the observation that the key computation common to most neural network layers is a vector/matrix product, we propose a fast locality-sensitive hashing technique to approximate the actual dot product enabling us to scale up the training and inference to millions of output classes. We evaluate our technique on three diverse large-scale recognition tasks and show that our approach can train large-scale models at a faster rate (in terms of steps/total time) compared to baseline methods.


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Factorial Hidden Markov Models for Learning Representations of Natural Language

Feb 18, 2014
Anjan Nepal, Alexander Yates

Most representation learning algorithms for language and image processing are local, in that they identify features for a data point based on surrounding points. Yet in language processing, the correct meaning of a word often depends on its global context. As a step toward incorporating global context into representation learning, we develop a representation learning algorithm that incorporates joint prediction into its technique for producing features for a word. We develop efficient variational methods for learning Factorial Hidden Markov Models from large texts, and use variational distributions to produce features for each word that are sensitive to the entire input sequence, not just to a local context window. Experiments on part-of-speech tagging and chunking indicate that the features are competitive with or better than existing state-of-the-art representation learning methods.

* 12 pages, 2 tables, ICLR-2014 

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Cognitive Principles in Robust Multimodal Interpretation

Sep 28, 2011
J. Y. Chai, Z. Prasov, S. Qu

Multimodal conversational interfaces provide a natural means for users to communicate with computer systems through multiple modalities such as speech and gesture. To build effective multimodal interfaces, automated interpretation of user multimodal inputs is important. Inspired by the previous investigation on cognitive status in multimodal human machine interaction, we have developed a greedy algorithm for interpreting user referring expressions (i.e., multimodal reference resolution). This algorithm incorporates the cognitive principles of Conversational Implicature and Givenness Hierarchy and applies constraints from various sources (e.g., temporal, semantic, and contextual) to resolve references. Our empirical results have shown the advantage of this algorithm in efficiently resolving a variety of user references. Because of its simplicity and generality, this approach has the potential to improve the robustness of multimodal input interpretation.

* Journal Of Artificial Intelligence Research, Volume 27, pages 55-83, 2006 

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Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities

Jul 29, 2004
Peter D. Turney

This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system approaches WSD as a classical supervised machine learning problem, using familiar tools such as the Weka machine learning software and Brill's rule-based part-of-speech tagger. Head words are represented as feature vectors with several hundred features. Approximately half of the features are syntactic and the other half are semantic. The main novelty in the system is the method for generating the semantic features, based on word \hbox{co-occurrence} probabilities. The probabilities are estimated using the Waterloo MultiText System with a corpus of about one terabyte of unlabeled text, collected by a web crawler.

* Proceedings of the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL-3), (2004), Barcelona, Spain, 239-242 
* related work available at http://purl.org/peter.turney/ 

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Polyphonic pitch detection with convolutional recurrent neural networks

Feb 04, 2022
Carl Thomé, Sven Ahlbäck

Recent directions in automatic speech recognition (ASR) research have shown that applying deep learning models from image recognition challenges in computer vision is beneficial. As automatic music transcription (AMT) is superficially similar to ASR, in the sense that methods often rely on transforming spectrograms to symbolic sequences of events (e.g. words or notes), deep learning should benefit AMT as well. In this work, we outline an online polyphonic pitch detection system that streams audio to MIDI by ConvLSTMs. Our system achieves state-of-the-art results on the 2007 MIREX multi-F0 development set, with an F-measure of 83\% on the bassoon, clarinet, flute, horn and oboe ensemble recording without requiring any musical language modelling or assumptions of instrument timbre.

* MIREX 2017 

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Star Temporal Classification: Sequence Classification with Partially Labeled Data

Jan 28, 2022
Vineel Pratap, Awni Hannun, Gabriel Synnaeve, Ronan Collobert

We develop an algorithm which can learn from partially labeled and unsegmented sequential data. Most sequential loss functions, such as Connectionist Temporal Classification (CTC), break down when many labels are missing. We address this problem with Star Temporal Classification (STC) which uses a special star token to allow alignments which include all possible tokens whenever a token could be missing. We express STC as the composition of weighted finite-state transducers (WFSTs) and use GTN (a framework for automatic differentiation with WFSTs) to compute gradients. We perform extensive experiments on automatic speech recognition. These experiments show that STC can recover most of the performance of supervised baseline when up to 70% of the labels are missing. We also perform experiments in handwriting recognition to show that our method easily applies to other sequence classification tasks.


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The futility of STILTs for the classification of lexical borrowings in Spanish

Sep 17, 2021
Javier de la Rosa

The first edition of the IberLEF 2021 shared task on automatic detection of borrowings (ADoBo) focused on detecting lexical borrowings that appeared in the Spanish press and that have recently been imported into the Spanish language. In this work, we tested supplementary training on intermediate labeled-data tasks (STILTs) from part of speech (POS), named entity recognition (NER), code-switching, and language identification approaches to the classification of borrowings at the token level using existing pre-trained transformer-based language models. Our extensive experimental results suggest that STILTs do not provide any improvement over direct fine-tuning of multilingual models. However, multilingual models trained on small subsets of languages perform reasonably better than multilingual BERT but not as good as multilingual RoBERTa for the given dataset.

* ADoBo 2021 Shared Task [email protected], CEUR Workshop Proceedings (Vol. 2943, pp. 947-955) 

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Identifying Offensive Expressions of Opinion in Context

Apr 27, 2021
Francielle Alves Vargas, Isabelle Carvalho, Fabiana Rodrigues de Góes

Classic information extraction techniques consist in building questions and answers about the facts. Indeed, it is still a challenge to subjective information extraction systems to identify opinions and feelings in context. In sentiment-based NLP tasks, there are few resources to information extraction, above all offensive or hateful opinions in context. To fill this important gap, this short paper provides a new cross-lingual and contextual offensive lexicon, which consists of explicit and implicit offensive and swearing expressions of opinion, which were annotated in two different classes: context dependent and context-independent offensive. In addition, we provide markers to identify hate speech. Annotation approach was evaluated at the expression-level and achieves high human inter-annotator agreement. The provided offensive lexicon is available in Portuguese and English languages.


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