Publications

SFB 1102Publications
44 entries
Varjokallio, Matti; Klakow, Dietrich

Unsupervised morph segmentation and statistical language models for vocabulary expansion Inproceedings

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics, pp. 175-180, Berlin, Germany, 2016.

This work explores the use of unsupervised morph segmentation along with statistical language models for the task of vocabulary expansion. Unsupervised vocabulary expansion has large potential for improving vocabulary coverage and performance in different natural language processing tasks, especially in lessresourced settings on morphologically rich languages. We propose a combination of unsupervised morph segmentation and statistical language models and evaluate on languages from the Babel corpus. The method is shown to perform well for all the evaluated languages when compared to the previous work on the task.

@inproceedings{varjokallio-klakow:2016:P16-2,
title = {Unsupervised morph segmentation and statistical language models for vocabulary expansion},
author = {Matti Varjokallio and Dietrich Klakow},
url = {http://anthology.aclweb.org/P16-2029},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages = {175-180},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {This work explores the use of unsupervised morph segmentation along with statistical language models for the task of vocabulary expansion. Unsupervised vocabulary expansion has large potential for improving vocabulary coverage and performance in different natural language processing tasks, especially in lessresourced settings on morphologically rich languages. We propose a combination of unsupervised morph segmentation and statistical language models and evaluate on languages from the Babel corpus. The method is shown to perform well for all the evaluated languages when compared to the previous work on the task.},
pubstate = {published},
type = {inproceedings}
}

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Project:   B4

Oualil, Youssef; Schulder, Marc; Helmke, Hartmut; Schmidt, Anna; Klakow, Dietrich

Real-Time Integration of Dynamic Context Information for Improving Automatic Speech Recognition Inproceedings

INTERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, 2015.
The use of prior situational/contextual knowledge about a given task can significantly improve automatic speech recognition (ASR) performance. This is typically done through adaptation of acoustic or language models if data is available or using knowledge-based rescoring. The main adaptation techniques, however, are either domain-specific, which makes them inadequate for other tasks, or static and offline, and therefore cannot deal with dynamic knowledge. To circumvent this problem, we propose a real-time system which dynamically integrates situational context into ASR. The context integration is done either post-recognition, in which case a weighted Levenshtein distance between the ASR hypotheses and the context information based on the ASR confidence scores is proposed to extract the most likely sequence of spoken words, or pre-recognition, where the search space is adjusted to the new situational knowledge through adaptation of the finite state machine modeling the spoken language. Experiments conducted on 3 hours of Air Traffic Control (ATC) data achieved a 51% reduction of the Command Error Rate (CmdER) which is used as evaluation metric in the ATC domain.

@inproceedings{youalil_interspeech_2015,
title = {Real-Time Integration of Dynamic Context Information for Improving Automatic Speech Recognition},
author = {Youssef Oualil and Marc Schulder and Hartmut Helmke and Anna Schmidt and Dietrich Klakow},
url = {https://core.ac.uk/display/31018097},
year = {2015},
date = {2015},
booktitle = {INTERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, Dresden, Germany},
abstract = {

The use of prior situational/contextual knowledge about a given task can significantly improve automatic speech recognition (ASR) performance. This is typically done through adaptation of acoustic or language models if data is available or using knowledge-based rescoring. The main adaptation techniques, however, are either domain-specific, which makes them inadequate for other tasks, or static and offline, and therefore cannot deal with dynamic knowledge. To circumvent this problem, we propose a real-time system which dynamically integrates situational context into ASR. The context integration is done either post-recognition, in which case a weighted Levenshtein distance between the ASR hypotheses and the context information based on the ASR confidence scores is proposed to extract the most likely sequence of spoken words, or pre-recognition, where the search space is adjusted to the new situational knowledge through adaptation of the finite state machine modeling the spoken language. Experiments conducted on 3 hours of Air Traffic Control (ATC) data achieved a 51% reduction of the Command Error Rate (CmdER) which is used as evaluation metric in the ATC domain.
},
pubstate = {published},
type = {inproceedings}
}

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Project:   B4

Greenberg, Clayton; Sayeed, Asad; Demberg, Vera

Improving Unsupervised Vector-Space Thematic Fit Evaluation via Role-Filler Prototype Clustering Inproceedings

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, pp. 21-31, Denver, Colorado, 2015.

Most recent unsupervised methods in vector space semantics for assessing thematic fit (e.g. Erk, 2007; Baroni and Lenci, 2010; Sayeed and Demberg, 2014) create prototypical rolefillers without performing word sense disambiguation. This leads to a kind of sparsity problem: candidate role-fillers for different senses of the verb end up being measured by the same “yardstick”, the single prototypical role-filler.

In this work, we use three different feature spaces to construct robust unsupervised models of distributional semantics. We show that correlation with human judgements on thematic fit estimates can be improved consistently by clustering typical role-fillers and then calculating similarities of candidate rolefillers with these cluster centroids. The suggested methods can be used in any vector space model that constructs a prototype vector from a non-trivial set of typical vectors

@inproceedings{greenberg-sayeed-demberg:2015:NAACL-HLT,
title = {Improving Unsupervised Vector-Space Thematic Fit Evaluation via Role-Filler Prototype Clustering},
author = {Clayton Greenberg and Asad Sayeed and Vera Demberg},
url = {http://www.aclweb.org/anthology/N15-1003},
year = {2015},
date = {2015},
booktitle = {Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages = {21-31},
publisher = {Association for Computational Linguistics},
address = {Denver, Colorado},
abstract = {Most recent unsupervised methods in vector space semantics for assessing thematic fit (e.g. Erk, 2007; Baroni and Lenci, 2010; Sayeed and Demberg, 2014) create prototypical rolefillers without performing word sense disambiguation. This leads to a kind of sparsity problem: candidate role-fillers for different senses of the verb end up being measured by the same “yardstick”, the single prototypical role-filler. In this work, we use three different feature spaces to construct robust unsupervised models of distributional semantics. We show that correlation with human judgements on thematic fit estimates can be improved consistently by clustering typical role-fillers and then calculating similarities of candidate rolefillers with these cluster centroids. The suggested methods can be used in any vector space model that constructs a prototype vector from a non-trivial set of typical vectors},
pubstate = {published},
type = {inproceedings}
}

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Projects:   B2 B4

Greenberg, Clayton; Demberg, Vera; Sayeed, Asad

Verb Polysemy and Frequency Effects in Thematic Fit Modeling Inproceedings

Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics, Association for Computational Linguistics, pp. 48-57, Denver, Colorado, 2015.

While several data sets for evaluating thematic fit of verb-role-filler triples exist, they do not control for verb polysemy. Thus, it is unclear how verb polysemy affects human ratings of thematic fit and how best to model that. We present a new dataset of human ratings on high vs. low-polysemy verbs matched for verb frequency, together with high vs. low-frequency and well-fitting vs. poorly-fitting patient rolefillers. Our analyses show that low-polysemy verbs produce stronger thematic fit judgements than verbs with higher polysemy. Rolefiller frequency, on the other hand, had little effect on ratings. We show that these results can best be modeled in a vector space using a clustering technique to create multiple prototype vectors representing different “senses” of the verb.

@inproceedings{greenberg-demberg-sayeed:2015:CMCL,
title = {Verb Polysemy and Frequency Effects in Thematic Fit Modeling},
author = {Clayton Greenberg and Vera Demberg and Asad Sayeed},
url = {http://www.aclweb.org/anthology/W15-1106},
year = {2015},
date = {2015-06-01},
booktitle = {Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics},
pages = {48-57},
publisher = {Association for Computational Linguistics},
address = {Denver, Colorado},
abstract = {While several data sets for evaluating thematic fit of verb-role-filler triples exist, they do not control for verb polysemy. Thus, it is unclear how verb polysemy affects human ratings of thematic fit and how best to model that. We present a new dataset of human ratings on high vs. low-polysemy verbs matched for verb frequency, together with high vs. low-frequency and well-fitting vs. poorly-fitting patient rolefillers. Our analyses show that low-polysemy verbs produce stronger thematic fit judgements than verbs with higher polysemy. Rolefiller frequency, on the other hand, had little effect on ratings. We show that these results can best be modeled in a vector space using a clustering technique to create multiple prototype vectors representing different “senses” of the verb.},
pubstate = {published},
type = {inproceedings}
}

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Projects:   B2 B4

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