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Statistical machine translation and its challenges 8

Statistical machine translation and its challenges

引用

8th International Conference on Spoken language Processing, ICSLP 2004

作者： Ney, Hermann Lehrstuhl für Informatik VI Human Language Technology and Pattern Recognition RWTH Aachen - University of Technology AachenD-52056 Germany

In addition to speech recognition and syntactic parsing, during the last 10 years, the statistical approach has found widespread use in machine translation of both written language and spoken language. In many comparative evaluations, the statistical approach was found to be competitive or superior to the existing conventional approaches. Since the first statistical approach was proposed at the end of the 80s, many attempts have been made to improve the state of the art. Like other natural language processing tasks, machine translation requires four major components: a decision rule, a set of probability models, a training criterion and an efficient generation of the target sentence. We will consider each of these four components in more detail and point out promising research directions.

关键词： Machine translation

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Phrase-based statistical machine translation 25th

引用

25th Annual German Conference on Artificial Intelligence, KI 2002

作者： Zens, Richard Josef Och, Franz Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen – University of Technology Germany

ISBN: (纸本)3540441859

This paper is based on the work carried out in the framework of the Verbmobil project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches. In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is that it does not take contextual information into account for the translation decisions. We will present a translation model that is based on bilingual phrases to explicitly model the local *** will show that this model performs better than the single-word based *** will compare monotone and non-monotone search for this model and we will investigate the benefit of using the sum criterion instead of the maximum approximation. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Computer aided language translation

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Investigation on data adaptation techniques for neural named entity recognition 59

Investigation on data adaptation techniques for neural named...

引用

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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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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Online optimisation of log-linear weights in interactive machine translation 9

Online optimisation of log-linear weights in interactive mac...

引用

9th International Conference on language Resources and Evaluation, LREC 2014

作者： Rios, Mara Chinea Sanchis-Trilles, Germán Ortiz-Martínez, Daniel Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Universitat Politècnica de València Camino de Vera s/n Valencia Spain

ISBN: (纸本)9782951740884

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the - art machine translation system underlying an interactive machine translation framework, with encouraging results.

关键词： Machine translation

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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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Efficient Training of Neural Transducer for Speech recognition 23

Efficient Training of Neural Transducer for Speech Recogniti...

引用

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

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

引用

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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Robust online multi-channel speech recognition

Robust online multi-channel speech recognition

引用

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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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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