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The AFRL-MITLL WMT16 news-translation task systems

Published in:
Proc. First Conf. on Machine Translation, Vol. 2, 11-12 August 2016, pp. 296-302.

Summary

This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology generation.
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Summary

This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology generation.

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The MITLL-AFRL IWSLT 2015 Systems

Summary

This report summarizes the MITLL-AFRL MT, ASR and SLT systems and the experiments run using them during the 2015 IWSLT evaluation campaign. We build on the progress made last year, and additionally experimented with neural MT, unknown word processing, and system combination. We applied these techniques to translating Chinese to English and English to Chinese. ASR systems are also improved by reining improvements developed last year. Finally, we combine our ASR and MT systems to produce a English to Chinese SLT system.
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Summary

This report summarizes the MITLL-AFRL MT, ASR and SLT systems and the experiments run using them during the 2015 IWSLT evaluation campaign. We build on the progress made last year, and additionally experimented with neural MT, unknown word processing, and system combination. We applied these techniques to translating Chinese to...

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The AFRL-MITLL WMT15 System: there's more than one way to decode it!

Published in:
Proc. 10th Workshop on Statistical Machine Translation, 17-18 September 2015, pp. 112-9.

Summary

This paper describes the AFRL-MITLL statistical MT systems and the improvements that were developed during the WMT15 evaluation campaign. As part of these efforts we experimented with a number of extensions to the standard phrase-based model that improve performance on the Russian to English translation task creating three submission systems with different decoding strategies. Out of vocabulary words were addressed with named entity postprocessing.
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Summary

This paper describes the AFRL-MITLL statistical MT systems and the improvements that were developed during the WMT15 evaluation campaign. As part of these efforts we experimented with a number of extensions to the standard phrase-based model that improve performance on the Russian to English translation task creating three submission systems...

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The MITLL/AFRL IWSLT-2014 MT System

Summary

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run using them during the 2014 IWSLT evaluation campaign. Our MT system is much improved over last year, owing to integration of techniques such as PRO and DREM optimization, factored language models, neural network joint model rescoring, multiple phrase tables, and development set creation. We focused our efforts this year on the tasks of translating from Arabic, Russian, Chinese, and Farsi into English, as well as translating from English to French. ASR performance also improved, partly due to increased efforts with deep neural networks for hybrid and tandem systems. Work focused on both the English and Italian ASR tasks.
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Summary

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run using them during the 2014 IWSLT evaluation campaign. Our MT system is much improved over last year, owing to integration of techniques such as PRO and DREM optimization, factored language models, neural network joint model rescoring, multiple...

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Using deep belief networks for vector-based speaker recognition

Published in:
INTERSPEECH 2014: 15th Annual Conf. of the Int. Speech Communication Assoc., 14-18 September 2014.

Summary

Deep belief networks (DBNs) have become a successful approach for acoustic modeling in speech recognition. DBNs exhibit strong approximation properties, improved performance, and are parameter efficient. In this work, we propose methods for applying DBNs to speaker recognition. In contrast to prior work, our approach to DBNs for speaker recognition starts at the acoustic modeling layer. We use sparse-output DBNs trained with both unsupervised and supervised methods to generate statistics for use in standard vector-based speaker recognition methods. We show that a DBN can replace a GMM UBM in this processing. Methods, qualitative analysis, and results are given on a NIST SRE 2012 task. Overall, our results show that DBNs show competitive performance to modern approaches in an initial implementation of our framework.
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Summary

Deep belief networks (DBNs) have become a successful approach for acoustic modeling in speech recognition. DBNs exhibit strong approximation properties, improved performance, and are parameter efficient. In this work, we propose methods for applying DBNs to speaker recognition. In contrast to prior work, our approach to DBNs for speaker recognition...

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Talking Head Detection by Likelihood-Ratio Test(220.2 KB)

Published in:
Second Workshop on Speech, Language, Audio in Multimedia

Summary

Detecting accurately when a person whose face is visible in an audio-visual medium is the audible speaker is an enabling technology with a number of useful applications. The likelihood-ratio test formulation and feature signal processing employed here allow the use of high-dimensional feature sets in the audio and visual domain, and the approach appears to have good detection performance for AV segments as short as a few seconds.
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Summary

Detecting accurately when a person whose face is visible in an audio-visual medium is the audible speaker is an enabling technology with a number of useful applications. The likelihood-ratio test formulation and feature signal processing employed here allow the use of high-dimensional feature sets in the audio and visual domain...

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Talking head detection by likelihood-ratio test

Published in:
2nd Int. Workshop on Speech, Language and Audio in Multimedia, SLAM, 11-12 September 2014.

Summary

Detecting accurately when a person whose face is visible in an audio-visual medium is the audible speaker is an enabling technology with a number of useful applications. These include fused audio/visual speaker recognition, AV (audio/visual) segmentation and diarization as well as AV synchronization. The likelihood-ratio test formulation and feature signal processing employed here allow the use of high-dimensional feature sets in the audio and visual domain, and the approach appears to have good detection performance for AV segments as short as a few seconds. Computation costs for the resulting algorithm are modest, typically much less than the front-end face detection system. While the resulting system requires model training, only true condition training (i.e. video where the talking speaker is audible) is required.
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Summary

Detecting accurately when a person whose face is visible in an audio-visual medium is the audible speaker is an enabling technology with a number of useful applications. These include fused audio/visual speaker recognition, AV (audio/visual) segmentation and diarization as well as AV synchronization. The likelihood-ratio test formulation and feature signal...

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Content+context=classification: examining the roles of social interactions and linguist content in Twitter user classification

Published in:
Proc. Second Workshop on Natural Language Processing for Social Media, SocialNLP, 24 August 2014, pp. 59-65.

Summary

Twitter users demonstrate many characteristics via their online presence. Connections, community memberships, and communication patterns reveal both idiosyncratic and general properties of users. In addition, the content of tweets can be critical for distinguishing the role and importance of a user. In this work, we explore Twitter user classification using context and content cues. We construct a rich graph structure induced by hashtags and social communications in Twitter. We derive features from this graph structure - centrality, communities, and local flow of information. In addition, we perform detailed content analysis on tweets looking at offensiveness and topics. We then examine user classification and the role of feature types (context, content) and learning methods (propositional, relational) through a series of experiments on annotated data. Our work contrasts with prior approaches in that we use relational learning and alternative, non-specialized feature sets. Our goal is to understand how both content and context are predictive of user characteristics. Experiments demonstrate that the best performance for user classification uses relational learning with varying content and context features.
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Summary

Twitter users demonstrate many characteristics via their online presence. Connections, community memberships, and communication patterns reveal both idiosyncratic and general properties of users. In addition, the content of tweets can be critical for distinguishing the role and importance of a user. In this work, we explore Twitter user classification using...

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Exploiting morphological, grammatical, and semantic correlates for improved text difficulty assessment

Author:
Published in:
Proc. 9th Workshop on Innovative Use of NLP for Building Educational Applications, 26 June 2014, pp. 155-162.

Summary

We present a low-resource, language-independent system for text difficulty assessment. We replicate and improve upon a baseline by Shen et al. (2013) on the Interagency Language Roundtable (ILR) scale. Our work demonstrates that the addition of morphological, information theoretic, and language modeling features to a traditional readability baseline greatly benefits our performance. We use the Margin-Infused Relaxed Algorithm and Support Vector Machines for experiments on Arabic, Dari, English, and Pashto, and provide a detailed analysis of our results.
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Summary

We present a low-resource, language-independent system for text difficulty assessment. We replicate and improve upon a baseline by Shen et al. (2013) on the Interagency Language Roundtable (ILR) scale. Our work demonstrates that the addition of morphological, information theoretic, and language modeling features to a traditional readability baseline greatly benefits...

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A new multiple choice comprehension test for MT

Published in:
Automatic and Manual Metrics for Operation Translation Evaluation Workshop, 9th Int. Conf. on Language Resources and Evaluation (LREC 2014), 26 May 2014.

Summary

We present results from a new machine translation comprehension test, similar to those developed in previous work (Jones et al., 2007). This test has documents in four conditions: (1) original English documents; (2) human translations of the documents into Arabic; conditions (3) and (4) are machine translations of the Arabic documents into English from two different MT systems. We created two forms of the test: Form A has the original English documents and output from the two Arabic-to-English MT systems. Form B has English, Arabic, and one of the MT system outputs. We administered the comprehension test to three subject types recruited in the greater Boston area: (1) native English speakers with no Arabic skills, (2) Arabic language learners, and (3) Native Arabic speakers who also have English language skills. There were 36 native English speakers, 13 Arabic learners, and 11 native Arabic speakers with English skills. Subjects needed an average of 3.8 hours to complete the test, which had 191 questions and 59 documents. Native English speakers with no Arabic skills saw Form A. Arabic learners and native Arabic speakers saw form B.
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Summary

We present results from a new machine translation comprehension test, similar to those developed in previous work (Jones et al., 2007). This test has documents in four conditions: (1) original English documents; (2) human translations of the documents into Arabic; conditions (3) and (4) are machine translations of the Arabic...

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