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检索条件"机构=Human Language Technology and Pattern"

共 386 条记录，以下是121-130 订阅

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ValenTo: Sentiment Analysis of Figurative language Tweets with Irony and Sarcasm 9

ValenTo: Sentiment Analysis of Figurative Language Tweets wi...

引用

9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Farías, Delia Irazú Hernández Sulis, Emilio Patti, Viviana Ruffo, Giancarlo Bosco, Cristina Universitat Politècnica de València Pattern Recognition and Human Language Technology Spain University of Turin Dipartimento di Informatica Italy

ISBN: (纸本)9781941643402

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only word-sentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets. © 2015 Association for Computational Linguistics

关键词： Sentiment analysis

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Investigation on data adaptation techniques for neural named entity recognition 59

Investigation on data adaptation techniques for neural named...

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2021 Student Research Workshop, SRW 2021 at the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural language Processing, ACL-IJCNLP 2021

作者： Tokarchuk, Evgeniia Thulke, David Wang, Weiyue Dugast, Christian Ney, Hermann Informatics Institute University of Amsterdam Netherlands Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781954085558

Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks. © 2021 Association for Computational Linguistics.

关键词： Data handling

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Vector space models for phrase-based machine translation 8

Vector space models for phrase-based machine translation

引用

8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014

作者： Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This paper investigates the application of vector space models (VSMs) to the standard phrase-based machine translation pipeline. VSMs are models based on continuous word representations embedded in a vector space. We exploit word vectors to augment the phrase table with new inferred phrase pairs. This helps reduce out-of-vocabulary (OOV) words. In addition, we present a simple way to learn bilingually-constrained phrase vectors. The phrase vectors are then used to provide additional scoring of phrase pairs, which fits into the standard log-linear framework of phrase-based statistical machine translation. Both methods result in significant improvements over a competitive in-domain baseline applied to the Arabic-to-English task of IWSLT 2013. © 2014 Association for Computational Linguistics

关键词： Vectors

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Translation modeling with bidirectional recurrent neural networks

Translation modeling with bidirectional recurrent neural net...

引用

2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Sundermeyer, Martin Alkhouli, Tamer Wuebker, Joern Ney, Hermann Human Language Technology Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German!English, BOLT Arabic!English and Chinese!English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists. © 2014 Association for Computational Linguistics.

关键词： Recurrent neural networks

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The CTC and its intriguing "blank" label: Comparative study of neural network training methods for handwriting recognition

The CTC and its intriguing "blank" label: Comparative study ...

引用

Conference en Recherche d'Informations et Applications, CORIA 2016, 13th French Information Retrieval Conference, CIFED 2016, et Colloque International Francophone sur l'Ecrit et le Document - Conference on Information Retrieval and its Applications, CORIA 2016, 13th French Information Retrieval Conference, CIFED 2016, and International French-Speaking Colloquium on Writing and Documentation

作者： Bluche, Théodore Kermorvant, Christopher Ney, Hermann Louradour, Jérôme A2iA SA Paris France Teklia SASU Paris France Human Language Technology and Pattern Recognition RWTH Aachen Allemagne Germany

In recent years, Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) trained with the Connectionist Temporal Classification (CTC) objective won many international handwriting recognition evaluations. The CTC algorithm is based on a forward-backward procedure, avoiding the need of a segmentation of the input before training. The network outputs are characters labels, and a special non-character label. On the other hand, in the hybrid Neural Network / Hidden Markov Models (NN/HMM) framework, networks are trained with framewise criteria to predict state labels. In this paper, we show that CTC training is close to forward-backward training of NN/HMMs, and can be extended to more standard HMM topologies. We apply this method to Multi-Layer Perceptrons (MLPs), and investigate the properties of CTC, especially the role of the special label.

关键词： Character recognition

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Efficient Training of Neural Transducer for Speech Recognition 23

Efficient Training of Neural Transducer for Speech Recogniti...

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23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022

作者： Zhou, Wei Michel, Wilfried Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52074 Aachen Germany AppTek GmbH 52062 Aachen Germany

As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated neural network models of growing size and increasing training epochs. While strong computation resources seem to be the prerequisite of training superior models, we try to overcome it by carefully designing a more efficient training pipeline. In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period. The effectiveness of each stage is experimentally verified on both Librispeech and Switchboard corpora. The proposed pipeline is able to train transducer models approaching state-of-the-art performance with a single GPU in just 2-3 weeks. Our best conformer transducer achieves 4.1% WER on Librispeech test-other with only 35 epochs of training. Copyright © 2022 ISCA.

关键词： Speech recognition

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Enhancing and Adversarial: Improve ASR with Speaker Labels 48

Enhancing and Adversarial: Improve ASR with Speaker Labels

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhou, Wei Wu, Haotian Xu, Jingjing Zeineldeen, Mohammad Luscher, Christoph Schluter, Ralf Ney, Hermann Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany AppTek GmbH Aachen52062 Germany

ISBN: (纸本)9781728163277

ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain variance towards domain-aware/agnostic ASR, respectively. In this work, we study how to best apply these two opposite objectives with speaker labels to improve conformer-based ASR. We also propose a novel adaptive gradient reversal layer for stable and effective adversarial training without tuning effort. Detailed analysis and experimental verification are conducted to show the optimal positions in the ASR neural network (NN) to apply speaker enhancing and adversarial training. We also explore their combination for further improvement, achieving the same performance as i-vectors plus adversarial training. Our best speaker-based MTL achieves 7% relative improvement on the Switchboard Hub5'00 set. We also investigate the effect of such speaker-based MTL w.r.t. cleaner dataset and weaker ASR NN. © 2023 IEEE.

关键词： Linearization

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Robust online multi-channel speech recognition

Robust online multi-channel speech recognition

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12. ITG-Fachtagung Sprachkommunikation - 12th ITG Conference on Speech Communication

作者： Kitza, Markus Zeyer, Albert Schlüter, Ralf Heymann, Jahn Haeb-Umbach, Reinhold Human Language Technology and Pattern Recognition RWTH Aachen Aachen52074 Germany Department of Communications Engineering Paderborn Paderborn University Paderborn33098 Germany

ISBN: (纸本)9783800742752

In this paper we present a system for robust online far-field multi-channel speech recognition with minimal assumptions on microphone configuration and target location. We employ an online-enabled Generalized Eigenvalue (GEV) beamformer and a Long Short-Term Memory (LSTM) network to robustly calculate the signal statistics necessary for the beamforming operation in the front-end. After multiple channels have been condensed to one, a Bidirectional Long Short-Term Memory (BLSTM) acoustic model is applied on a running window of input speech. This enables online decoding in combination with the beamforming front-end. To assess the performance of the system we test it on the real evaluation set of the CHiME 3 data where we achieve a Word Error Rate (WER) of 10.4 %. © 2016 VDE VERLAG GMBH.

关键词： Speech recognition

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Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers 48

Lattice-Free Sequence Discriminative Training for Phoneme-Ba...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Zijian Zhou, Wei Schluter, Ralf Ney, Hermann Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany AppTek GmbH Aachen52062 Germany

ISBN: (纸本)9781728163277

Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid models, are rarely investigated in RNN-Transducers. In this work, we propose three lattice-free training objectives, namely lattice-free maximum mutual information, lattice-free segment-level minimum Bayes risk, and lattice-free minimum Bayes risk, which are used for the final posterior output of the phoneme-based neural transducer with a limited context dependency. Compared to criteria using N-best lists, lattice-free methods eliminate the decoding step for hypotheses generation during training, which leads to more efficient training. Experimental results show that lattice-free methods gain up to 6.5% relative improvement in word error rate compared to a sequence-level cross-entropy trained model. Compared to the N-best-list based minimum Bayes risk objectives, lattice-free methods gain 40% - 70% relative training time speedup with a small degradation in performance. © 2023 IEEE.

关键词： neural transducer sequence discriminative training Speech recognition

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Exploring multimedia databases via optimization-based relevance feedback and the earth mover's distance

Exploring multimedia databases via optimization-based releva...

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ACM 18th International Conference on Information and Knowledge Management, CIKM 2009

作者： Wichterich, Marc Beecks, Christian Sundermeyer, Martin Seidl, Thomas Data Management and Data Exploration Group RWTH Aachen University Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781605585123

Determining similar objects is a fundamental operation both in data mining tasks such as clustering and in query-driven object retrieval. By definition of similarity search, query objects can only be imprecise descriptions of what users are looking for in a database, and even high-quality similarity measures can only be approximations of the users' notion of similarity. To overcome these shortcomings, iterative query refinement systems have been proposed. They utilize user feedback regarding the relevance of intermediate results to adapt the query object and/or the similarity measure. We propose an optimization-based relevance feedback approach for adaptable distance measures - focusing on the Earth Mover's Distance. Our technique enables quicker iterative database exploration as shown by our experiments. Copyright 2009 ACM.

关键词： Database systems

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