Publications

Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.

• https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787

@inproceedings{Fankhauser2016,
title = {Topical Diversification over Time in the Royal Society Corpus},
author = {Peter Fankhauser and J{\"o}rg Knappen and Elke Teich},
url = {https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing

@inproceedings{Kermes2016a,
title = {The Royal Society Corpus. Towards a high-quality resource for studying diachronic variation in scientific writing},
author = {Hannah Kermes and J{\"o}rg Knappen and Ashraf Khamis and Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {

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We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic
measures of entropy and surprisal.

• https://aclanthology.org/W16-2121
• https://doi.org/10.18653/v1/W16-2121

@inproceedings{Degaetano-Ortlieb2016a,
title = {Information-based modeling of diachronic linguistic change: from typicality to productivity},
author = {Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://aclanthology.org/W16-2121},
doi = {https://doi.org/10.18653/v1/W16-2121},
year = {2016},
date = {2016},
booktitle = {Proceedings of Language Technologies for the Socio-Economic Sciences and Humanities (LATECH'16), Association for Computational Linguistics (ACL)},
pages = {165-173},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic measures of entropy and surprisal.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302025/

@inproceedings{Ankener2016,
title = {The influence of visual context on predictions in sentence processing: Evidence from ICA},
author = {Maria Staudte},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302025/},
year = {2016},
date = {2016},
booktitle = {Proceedings at the Language and Perception International Conference},
address = {Trondheim, Norwegen},
abstract = {

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• ESLP2016_book_of_abstracts_2     (10.01MB)

@inproceedings{sekicki2016b,
title = {Cost and Gains of Using Visual Context for Referent Prediction},
author = {Maria Staudte},
year = {2016},
date = {2016-10-18},
booktitle = {Proceedings of the 9th Embodied and Situated Language Processing Conference (ESLP)},
address = {Pucón},
pubstate = {published},
type = {inproceedings}
}

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We investigated the effect of visual context on cognitive load (CL) that is induced by prediction forming during sentence processing, using a novel measure of CL: the Index of Cognitive Activity. We conducted two experiments, one including only linguistic stimuli (LING) and one with the additional visual context of four potential target objects (VIS). Noun predictability was modulated by verb constraint (ironable vs. describable objects) and thematic fit; and further by visual competitors (two ironable vs. four describable objects).
“The woman (1) irons / (2) describes soon the (a) t-shirt / (b) sock.“
We found lower CL on the noun in (1a) compared to (1b) in both studies, suggesting that after “iron“, “t-shirt“ was more predictable, and hence easier to process, than “sock“. More importantly, VIS findings show higher CL on “iron“ compared to “describe“, suggesting that visual context allowed for active exclusion of two non-ironable targets. Conversely, CL on nouns was lower when following “iron“ than “describe“, due to only one ironable competitor compared to three describable competitors. These findings suggest that the presence of visual context alters the distribution of CL during sentence processing. Future work includes gaze cues as additional information, potentially further affecting CL distribution.

@inproceedings{sekicki2016,
title = {Language Processing: Cognitive Load with(out) Visual Context},
author = {Mirjana Sekicki and Christine Ankener and Maria Staudte},
year = {2016},
date = {2016-10-18},
booktitle = {Proceedings at the 22nd Annual Conference on Architectures and Mechanisms for Language Processing (AMLaP)},
address = {Bilbao, Spain},
abstract = {We investigated the effect of visual context on cognitive load (CL) that is induced by prediction forming during sentence processing, using a novel measure of CL: the Index of Cognitive Activity. We conducted two experiments, one including only linguistic stimuli (LING) and one with the additional visual context of four potential target objects (VIS). Noun predictability was modulated by verb constraint (ironable vs. describable objects) and thematic fit; and further by visual competitors (two ironable vs. four describable objects). ''The woman (1) irons / (2) describes soon the (a) t-shirt / (b) sock.'' We found lower CL on the noun in (1a) compared to (1b) in both studies, suggesting that after ''iron'', ''t-shirt'' was more predictable, and hence easier to process, than ''sock''. More importantly, VIS findings show higher CL on ''iron'' compared to ''describe'', suggesting that visual context allowed for active exclusion of two non-ironable targets. Conversely, CL on nouns was lower when following ''iron'' than ''describe'', due to only one ironable competitor compared to three describable competitors. These findings suggest that the presence of visual context alters the distribution of CL during sentence processing. Future work includes gaze cues as additional information, potentially further affecting CL distribution.},
pubstate = {published},
type = {inproceedings}
}

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Contexts that constrain upcoming words to some higher or lower extent can be composed differently but are typically all evaluated using cloze-probability (Rayner & Well, 1996). Less predicted words were found to correlate with more negative N400 (e.g., Frank et al., 2015; Kutas & Hillyard, 1984) and longer reading times (Rayner & Well, 1996; Smith & Levy, 2013). Recently, however, it has been suggested that predictability, as in cloze-probability, is only one influence on processing cost (e.g., DeLong et al., 2014). As DeLong et al. show, differences in plausibility of words with similar cloze-probability also affect processing of such words, reflected in different ERP components. This hints at a difference between frequency-based and deeper semantic processing. Moreover, a relatively novel measure, the Index of Cognitive Activity (ICA) capturing pupil jitter, has been linked to cognitive load and predictability (Demberg et al., 2013).

• CUNY2016     (0.06MB)
• https://www.coli.uni-saarland.de/~mirjana/papers/CUNY2016.pdf

@inproceedings{CUNY2016_A5,
title = {Low Predictability: An Empirical Comparison of Paradigms Used for Sentence Comprehension},
author = {Maria Staudte},
url = {https://www.coli.uni-saarland.de/~mirjana/papers/CUNY2016.pdf},
year = {2016},
date = {2016},
booktitle = {29th Annual Conference on Human Sentence Processing (CUNY)},
address = {Gainesville, FL},
abstract = {Contexts that constrain upcoming words to some higher or lower extent can be composed differently but are typically all evaluated using cloze-probability (Rayner & Well, 1996). Less predicted words were found to correlate with more negative N400 (e.g., Frank et al., 2015; Kutas & Hillyard, 1984) and longer reading times (Rayner & Well, 1996; Smith & Levy, 2013). Recently, however, it has been suggested that predictability, as in cloze-probability, is only one influence on processing cost (e.g., DeLong et al., 2014). As DeLong et al. show, differences in plausibility of words with similar cloze-probability also affect processing of such words, reflected in different ERP components. This hints at a difference between frequency-based and deeper semantic processing. Moreover, a relatively novel measure, the Index of Cognitive Activity (ICA) capturing pupil jitter, has been linked to cognitive load and predictability (Demberg et al., 2013).},
pubstate = {published},
type = {inproceedings}
}

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• http://www.frontiersin.org/language_sciences/10.3389/fpsyg.2016.00844/abstract
• https://doi.org/10.3389/fpsyg.2016.00844

@article{Zarcone2016,
title = {Salience and attention in surprisal-based accounts of language processing},
author = {Alessandra Zarcone and Marten van Schijndel and Jorrig Vogels and Vera Demberg},
url = {http://www.frontiersin.org/language_sciences/10.3389/fpsyg.2016.00844/abstract},
doi = {https://doi.org/10.3389/fpsyg.2016.00844},
year = {2016},
date = {2016},
journal = {Frontiers in Psychology},
volume = {7},
number = {844},
abstract = {

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A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in,
e.g., Bicknell et al. (2010), event-level knowledge does affect human expectations for verbal arguments. For example, the model should be able to predict that mechanics are likely to check tires, while journalists are more likely to check typos. Similarly, we would like to predict what locations are likely for playing football or playing flute in order to estimate the surprisal of actually-encountered locations. Furthermore, such a model can be used to provide a probability distribution over fillers for a thematic role which is not mentioned in the text at all.

To this end, we train two neural network models (an incremental one and a non-incremental one) on large amounts of automatically rolelabelled text. Our models are probabilistic and can handle several roles at once, which also enables them to learn interactions between different role fillers. Evaluation shows a drastic improvement over current state-of-the-art systems on modelling human thematic fit judgements, and we demonstrate via a sentence similarity task that the system learns highly useful embeddings.

• https://www.semanticscholar.org/paper/Event-participant-modelling-with-neural-networks-Tilk-Demberg/d08d663d7795c76bb008f539b1ac7caf8a9ef26c

@inproceedings{Tilk2016,
title = {Event participation modelling with neural networks},
author = {Ottokar Tilk and Vera Demberg and Asad Sayeed and Dietrich Klakow and Stefan Thater},
url = {https://www.semanticscholar.org/paper/Event-participant-modelling-with-neural-networks-Tilk-Demberg/d08d663d7795c76bb008f539b1ac7caf8a9ef26c},
year = {2016},
date = {2016},
publisher = {Conference on Empirical Methods in Natural Language Processing},
address = {Austin, Texas, USA},
abstract = {A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in, e.g., Bicknell et al. (2010), event-level knowledge does affect human expectations for verbal arguments. For example, the model should be able to predict that mechanics are likely to check tires, while journalists are more likely to check typos. Similarly, we would like to predict what locations are likely for playing football or playing flute in order to estimate the surprisal of actually-encountered locations. Furthermore, such a model can be used to provide a probability distribution over fillers for a thematic role which is not mentioned in the text at all. To this end, we train two neural network models (an incremental one and a non-incremental one) on large amounts of automatically rolelabelled text. Our models are probabilistic and can handle several roles at once, which also enables them to learn interactions between different role fillers. Evaluation shows a drastic improvement over current state-of-the-art systems on modelling human thematic fit judgements, and we demonstrate via a sentence similarity task that the system learns highly useful embeddings.},
pubstate = {published},
type = {inproceedings}
}

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Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.

• https://aclanthology.org/Q17-1003
• https://doi.org/10.1162/tacl_a_00044

@article{ashutoshTacl2016,
title = {Modeling Semantic Expectations: Using Script Knowledge for Referent Prediction},
author = {Ashutosh Modi and Ivan Titov and Vera Demberg and Asad Sayeed and Manfred Pinkal},
url = {https://aclanthology.org/Q17-1003},
doi = {https://doi.org/10.1162/tacl_a_00044},
year = {2016},
date = {2016},
journal = {Transactions of the Association for Computational Linguistics},
pages = {31-44},
publisher = {MIT Press},
address = {Cambridge, MA},
abstract = {Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.},
pubstate = {published},
type = {article}
}

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Semantic scripts is a conceptual representation which defines how events are organized into higher level activities. Practically all the previous approaches to inducing script knowledge from text relied on count-based techniques (e.g., generative models) and have not attempted to compositionally model events. In this work, we introduce a neural network model which relies on distributed compositional representations of events. The model captures statistical dependencies between events in a scenario, overcomes some of the shortcomings of previous approaches (e.g., by more effectively dealing with data sparsity) and outperforms count-based counterparts on the narrative cloze task.

• https://www.researchgate.net/publication/306093411_Event_Embeddings_for_Semantic_Script_Modeling

@inproceedings{modi:CONLL2016,
title = {Event Embeddings for Semantic Script Modeling},
author = {Ashutosh Modi},
url = {https://www.researchgate.net/publication/306093411_Event_Embeddings_for_Semantic_Script_Modeling},
year = {2016},
date = {2016-10-17},
booktitle = {Proceedings of the Conference on Computational Natural Language Learning (CoNLL)},
address = {Berlin, Germany},
abstract = {Semantic scripts is a conceptual representation which defines how events are organized into higher level activities. Practically all the previous approaches to inducing script knowledge from text relied on count-based techniques (e.g., generative models) and have not attempted to compositionally model events. In this work, we introduce a neural network model which relies on distributed compositional representations of events. The model captures statistical dependencies between events in a scenario, overcomes some of the shortcomings of previous approaches (e.g., by more effectively dealing with data sparsity) and outperforms count-based counterparts on the narrative cloze task.},
pubstate = {published},
type = {inproceedings}
}

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This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.

• 352_Paper     (0.62MB)
• https://aclanthology.org/L16-1555

@inproceedings{MODI16.352,
title = {InScript: Narrative texts annotated with script information},
author = {Ashutosh Modi and Tatjana Anikina and Simon Ostermann and Manfred Pinkal},
editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
url = {https://aclanthology.org/L16-1555},
year = {2016},
date = {2016-10-17},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
isbn = {978-2-9517408-9-1},
pages = {3485-3493},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.researchgate.net/publication/321621784_When_the_food_arrives_before_the_menu_Modeling_event-driven_surprisal_in_language_comprehension

@inproceedings{Venhuizen2016,
title = {When the food arrives before the menu: Modeling event-driven surprisal in language comprehension},
author = {Noortje Venhuizen and Harm Brouwer and Matthew W. Crocker},
url = {https://www.researchgate.net/publication/321621784_When_the_food_arrives_before_the_menu_Modeling_event-driven_surprisal_in_language_comprehension},
year = {2016},
date = {2016},
booktitle = {29th CUNY conference on Human Sentence Processing},
publisher = {Events in Language and Cognition workshops},
address = {University of Florida},
abstract = {

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• https://www.researchgate.net/publication/320988696_Reading_Between_the_Lines_The_Influence_of_Script_Knowledge_on_On-Line_Comprehension

@inproceedings{Rabs2016,
title = {Reading between the lines: The influence of script knowledge on on-line comprehension},
author = {Elisabeth Rabs and Heiner Drenhaus and Francesca Delogu and Matthew W. Crocker},
url = {https://www.researchgate.net/publication/320988696_Reading_Between_the_Lines_The_Influence_of_Script_Knowledge_on_On-Line_Comprehension},
year = {2016},
date = {2016},
booktitle = {29th CUNY conference on Human Sentence Processing},
publisher = {Events in Language and Cognition workshops},
address = {University of Florida},
abstract = {

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The MaryTTS system is a modular architecture text-to-speech (TTS) system whose development started around 15 years ago. This paper presents the MaryTTS entry for the Blizzard Challenge 2016. For this entry, we used the default configuration of MaryTTS based on the unit selection paradigm.

However, the architecture is currently undergoing a massive refactoring process in order to provide a more fully modular system. This will allow researchers to focus only on some part of the synthesis process. The current participation objective includes assessing the current baseline quality in order to evaluate any future improvements. These can be achieved more easily thanks to a more flexible and robust architecture. The results obtained in this challenge prove that our system is not obsolete, but improvements need to be made to maintain it in the state of the art in the future.

• MARYTTS_Blizzard2016     (0.23MB)
• https://www.semanticscholar.org/paper/The-MaryTTS-entry-for-the-Blizzard-Challenge-2016-Maguer-Steiner/62e04ad78ba1a531e419bea25cb9eb8799aaf07e

@inproceedings{LeMaguer2016BC,
title = {The MaryTTS entry for the Blizzard Challenge 2016},
author = {S{\'e}bastien Le Maguer and Ingmar Steiner},
url = {https://www.semanticscholar.org/paper/The-MaryTTS-entry-for-the-Blizzard-Challenge-2016-Maguer-Steiner/62e04ad78ba1a531e419bea25cb9eb8799aaf07e},
year = {2016},
date = {2016-09-16},
booktitle = {Blizzard Challenge},
address = {Cupertino, CA, USA},
abstract = {The MaryTTS system is a modular architecture text-to-speech (TTS) system whose development started around 15 years ago. This paper presents the MaryTTS entry for the Blizzard Challenge 2016. For this entry, we used the default configuration of MaryTTS based on the unit selection paradigm. However, the architecture is currently undergoing a massive refactoring process in order to provide a more fully modular system. This will allow researchers to focus only on some part of the synthesis process. The current participation objective includes assessing the current baseline quality in order to evaluate any future improvements. These can be achieved more easily thanks to a more flexible and robust architecture. The results obtained in this challenge prove that our system is not obsolete, but improvements need to be made to maintain it in the state of the art in the future.},
pubstate = {published},
type = {inproceedings}
}

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The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.

• https://aclanthology.org/D16-1154/

@inproceedings{Oualil2016,
title = {Long-short range context neural networks for language models},
author = {Youssef Oualil and Mittul Singh and Clayton Greenberg and Dietrich Klakow},
url = {https://aclanthology.org/D16-1154/},
year = {2016},
date = {2016},
publisher = {EMLP 2016},
abstract = {The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.},
pubstate = {published},
type = {inproceedings}
}

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Secure user identification is important for the increasing number of eyewear computers but limited input capabilities pose significant usability challenges for established knowledge-based schemes, such as passwords or PINs. We present SkullConduct, a biometric system that uses bone conduction of sound through the user’s skull as well as a microphone readily integrated into many of these devices, such as Google Glass. At the core of SkullConduct is a method to analyze the characteristic frequency response created by the user’s skull using a combination of Mel Frequency Cepstral Coefficient (MFCC) features as well as a computationally light-weight 1NN classifier. We report on a controlled experiment with 10 participants that shows that this frequency response is person-specific and stable — even when taking off and putting on the device multiple times — and thus serves as a robust biometric. We show that our method can identify users with 97.0% accuracy and authenticate them with an equal error rate of 6.9%, thereby bringing biometric user identification to eyewear computers equipped with bone conduction technology.

• http://doi.acm.org/10.1145/2858036.2858152
• https://doi.org/10.1145/2858036.2858152

@inproceedings{Schneegass:2016:SBU:2858036.2858152,
title = {SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull},
author = {Stefan Schneegass and Youssef Oualil and Andreas Bulling},
url = {http://doi.acm.org/10.1145/2858036.2858152},
doi = {https://doi.org/10.1145/2858036.2858152},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems},
isbn = {978-1-4503-3362-7},
pages = {1379-1384},
publisher = {ACM},
address = {New York, NY, USA},
abstract = {Secure user identification is important for the increasing number of eyewear computers but limited input capabilities pose significant usability challenges for established knowledge-based schemes, such as passwords or PINs. We present SkullConduct, a biometric system that uses bone conduction of sound through the user's skull as well as a microphone readily integrated into many of these devices, such as Google Glass. At the core of SkullConduct is a method to analyze the characteristic frequency response created by the user's skull using a combination of Mel Frequency Cepstral Coefficient (MFCC) features as well as a computationally light-weight 1NN classifier. We report on a controlled experiment with 10 participants that shows that this frequency response is person-specific and stable -- even when taking off and putting on the device multiple times -- and thus serves as a robust biometric. We show that our method can identify users with 97.0% accuracy and authenticate them with an equal error rate of 6.9%, thereby bringing biometric user identification to eyewear computers equipped with bone conduction technology.},
pubstate = {published},
type = {inproceedings}
}

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This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.

• http://www.aclweb.org/anthology/W/W16/W16-2301

@inproceedings{bojar-EtAl:2016:WMT1,
title = {Findings of the 2016 Conference on Machine Translation},
author = {Ondvrej Bojar and Rajen Chatterjee and Christian Federmann and Yvette Graham and Barry Haddow and Matthias Huck and Antonio Jimeno Yepes and Philipp Koehn and Varvara Logacheva and Christof Monz and Matteo Negri and Aurelie Neveol and Mariana Neves and Martin Popel and Matt Post and Raphael Rubino and Carolina Scarton and Lucia Specia and Marco Turchi and Karin Verspoor and Marcos Zampieri},
url = {http://www.aclweb.org/anthology/W/W16/W16-2301},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the First Conference on Machine Translation},
pages = {131-198},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.},
pubstate = {published},
type = {inproceedings}
}

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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.

• http://anthology.aclweb.org/P16-2029

@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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In this paper, we present Roleo, a web tool for visualizing the vector spaces generated by the evaluation of distributional memory (DM) models over thematic fit judgements. A thematic fit judgement is a rating of the selectional preference of a verb for an argument that fills a given thematic role. The DM approach to thematic fit judgements involves the construction of a sub-space in which a prototypical role-filler can be built for comparison to the noun being judged. We describe a publicly-accessible web tool that allows for querying and exploring these spaces as well as a technique for visualizing thematic fit sub-spaces efficiently for web use.

• P16-4024     (0.35MB)
• https://www.researchgate.net/publication/306093691_Roleo_Visualising_Thematic_Fit_Spaces_on_the_Web

@inproceedings{sayeed-hong-demberg:2016:P16-4,
title = {Roleo: Visualising Thematic Fit Spaces on the Web},
author = {Asad Sayeed and Xudong Hong and Vera Demberg},
url = {https://www.researchgate.net/publication/306093691_Roleo_Visualising_Thematic_Fit_Spaces_on_the_Web},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of ACL-2016 System Demonstrations},
pages = {139-144},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {In this paper, we present Roleo, a web tool for visualizing the vector spaces generated by the evaluation of distributional memory (DM) models over thematic fit judgements. A thematic fit judgement is a rating of the selectional preference of a verb for an argument that fills a given thematic role. The DM approach to thematic fit judgements involves the construction of a sub-space in which a prototypical role-filler can be built for comparison to the noun being judged. We describe a publicly-accessible web tool that allows for querying and exploring these spaces as well as a technique for visualizing thematic fit sub-spaces efficiently for web use.},
pubstate = {published},
type = {inproceedings}
}

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