Wolfgang Menzel


2021

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The Impact of Word Embeddings on Neural Dependency Parsing
Benedikt Adelmann | Wolfgang Menzel | Heike Zinsmeister
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

2020

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Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations
Özge Alacam | Eugen Ruppert | Amr Rekaby Salama | Tobias Staron | Wolfgang Menzel
Proceedings of the 12th Language Resources and Evaluation Conference

Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener. It consists of not only fixation parameters but also saccadic movement parameters that are time-locked to accompanying German utterances (with English translations). Additionally, it also contains symbolic knowledge (contextual) representations of the images to map the referring expressions onto the objects in corresponding images. Overall, the data was collected from 62 participants in three different experimental setups (86 systematically controlled sentence–image pairs and 1844 eye-movement recordings). Referential complexity was controlled by visual manipulations (e.g. number of objects, visibility of the target items, etc.), and by linguistic manipulations (e.g., the position of the disambiguating word in a sentence). This multimodal dataset, in which the three different sources of information namely eye-tracking, language, and visual environment are aligned, offers a test of various research questions not from only language perspective but also computer vision.

2018

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Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks
Tobias Staron | Özge Alaçam | Wolfgang Menzel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Text Completion using Context-Integrated Dependency Parsing
Amr Rekaby Salama | Özge Alaçam | Wolfgang Menzel
Proceedings of The Third Workshop on Representation Learning for NLP

Incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges that a successful communication system has to deal with. In this paper, we study text completion with a data set composed of sentences with gaps where a successful completion cannot be achieved through a uni-modal (language-based) approach. We present a solution based on a context-integrating dependency parser incorporating an additional non-linguistic modality. An incompleteness in one channel is compensated by information from another one and the parser learns the association between the two modalities from a multiple level knowledge representation. We examined several model variations by adjusting the degree of influence of different modalities in the decision making on possible filler words and their exact reference to a non-linguistic context element. Our model is able to fill the gap with 95.4% word and 95.2% exact reference accuracy hence the successful prediction can be achieved not only on the word level (such as mug) but also with respect to the correct identification of its context reference (such as mug 2 among several mug instances).

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Translation of Biomedical Documents with Focus on Spanish-English
Mirela-Stefania Duma | Wolfgang Menzel
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

For the WMT 2018 shared task of translating documents pertaining to the Biomedical domain, we developed a scoring formula that uses an unsophisticated and effective method of weighting term frequencies and was integrated in a data selection pipeline. The method was applied on five language pairs and it performed best on Portuguese-English, where a BLEU score of 41.84 placed it third out of seven runs submitted by three institutions. In this paper, we describe our method and results with a special focus on Spanish-English where we compare it against a state-of-the-art method. Our contribution to the task lies in introducing a fast, unsupervised method for selecting domain-specific data for training models which obtain good results using only 10% of the general domain data.

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The Benefit of Pseudo-Reference Translations in Quality Estimation of MT Output
Melania Duma | Wolfgang Menzel
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

In this paper, a novel approach to Quality Estimation is introduced, which extends the method in (Duma and Menzel, 2017) by also considering pseudo-reference translations as data sources to the tree and sequence kernels used before. Two variants of the system were submitted to the sentence level WMT18 Quality Estimation Task for the English-German language pair. They have been ranked 4th and 6th out of 13 systems in the SMT track, while in the NMT track ranks 4 and 5 out of 11 submissions have been reached.

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Graph Algebraic Combinatory Categorial Grammar
Sebastian Beschke | Wolfgang Menzel
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

This paper describes CCG/AMR, a novel grammar for semantic parsing of Abstract Meaning Representations. CCG/AMR equips Combinatory Categorial Grammar derivations with graph semantics by assigning each CCG combinator an interpretation in terms of a graph algebra. We provide an algorithm that induces a CCG/AMR from a corpus and show that it creates a compact lexicon with low ambiguity and achieves a robust coverage of 78% of the examined sentences under ideal conditions. We also identify several phenomena that affect any approach relying either on CCG or graph algebraic approaches for AMR parsing. This includes differences of representation between CCG and AMR, as well as non-compositional constructions that are not expressible through a monotonous construction process. To our knowledge, this paper provides the first analysis of these corpus issues.

2017

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Automatic Threshold Detection for Data Selection in Machine Translation
Mirela-Stefania Duma | Wolfgang Menzel
Proceedings of the Second Conference on Machine Translation

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UHH Submission to the WMT17 Quality Estimation Shared Task
Melania Duma | Wolfgang Menzel
Proceedings of the Second Conference on Machine Translation

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UHH Submission to the WMT17 Metrics Shared Task
Melania Duma | Wolfgang Menzel
Proceedings of the Second Conference on Machine Translation

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SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector
Mirela-Stefania Duma | Wolfgang Menzel
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our unsupervised knowledge-free approach to the SemEval-2017 Task 1 Competition. The proposed method makes use of Paragraph Vector for assessing the semantic similarity between pairs of sentences. We experimented with various dimensions of the vector and three state-of-the-art similarity metrics. Given a cross-lingual task, we trained models corresponding to its two languages and combined the models by averaging the similarity scores. The results of our submitted runs are above the median scores for five out of seven test sets by means of Pearson Correlation. Moreover, one of our system runs performed best on the Spanish-English-WMT test set ranking first out of 53 runs submitted in total by all participants.

2016

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Data Selection for IT Texts using Paragraph Vector
Mirela-Stefania Duma | Wolfgang Menzel
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2014

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Large-scale CCG Induction from the Groningen Meaning Bank
Sebastian Beschke | Yang Liu | Wolfgang Menzel
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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Because Size Does Matter: The Hamburg Dependency Treebank
Kilian A. Foth | Arne Köhn | Niels Beuck | Wolfgang Menzel
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present the Hamburg Dependency Treebank (HDT), which to our knowledge is the largest dependency treebank currently available. It consists of genuine dependency annotations, i. e. they have not been transformed from phrase structures. We explore characteristics of the treebank and compare it against others. To exemplify the benefit of large dependency treebanks, we evaluate different parsers on the HDT. In addition, a set of tools will be described which help working with and searching in the treebank.

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Incremental Predictive Parsing with TurboParser
Arne Köhn | Wolfgang Menzel
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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A New Syntactic Metric for Evaluation of Machine Translation
Melania Duma | Cristina Vertan | Wolfgang Menzel
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop

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Incremental and Predictive Dependency Parsing under Real-Time Conditions
Arne Köhn | Wolfgang Menzel
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2011

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Decision Strategies for Incremental POS Tagging
Niels Beuck | Arne Köhn | Wolfgang Menzel
Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011)

2009

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Co-Parsing with Competitive Models
Lidia Khmylko | Kilian A. Foth | Wolfgang Menzel
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

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Co-Parsing with Competitive Models
Lidia Khmylko | Kilian A. Foth | Wolfgang Menzel
Proceedings of the International Conference RANLP-2009

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Amharic Part-of-Speech Tagger for Factored Language Modeling
Martha Yifiru Tachbelie | Wolfgang Menzel
Proceedings of the International Conference RANLP-2009

2007

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Syllable-Based Speech Recognition for Amharic
Solomon Teferra Abate | Wolfgang Menzel
Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources

2006

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Guiding a Constraint Dependency Parser with Supertags
Kilian A. Foth | Tomas By | Wolfgang Menzel
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Hybrid Parsing: Using Probabilistic Models as Predictors for a Symbolic Parser
Kilian A. Foth | Wolfgang Menzel
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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The Benefit of Stochastic PP Attachment to a Rule-Based Parser
Kilian A. Foth | Wolfgang Menzel
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Robust Parsing: More with Less
Kilian Foth | Wolfgang Menzel
Proceedings of the Workshop on ROMAND 2006:Robust Methods in Analysis of Natural language Data

2004

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Interactive grammar development with WCDG
Kilian A. Foth | Michael Daum | Wolfgang Menzel
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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Automatic Transformation of Phrase Treebanks to Dependency Trees
Michael Daum | Kilian A. Foth | Wolfgang Menzel
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2003

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Subtree Parsing to Speed up Deep Analysis
Kilian Foth | Wolfgang Menzel
Proceedings of the Eighth International Conference on Parsing Technologies

Within a grammar formalism that treats syntax analysis as a global optimization problem, methods are investigated to improve parsing performance by recombining the solutions of smaller and easier subproblems. The robust nature of the formalism allows the application of this technique with little change to the original grammar.

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Constraint Based Integration of Deep and Shallow Parsing Techniques
Michael Daum | Kilian A. Foth | Wolfgang Menzel
10th Conference of the European Chapter of the Association for Computational Linguistics

2000

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The ISLE Corpus of Non-Native Spoken English
Wolfgang Menzel | Eric Atwell | Patrizia Bonaventura | Daniel Herron | Peter Howarth | Rachel Morton | Clive Souter
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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An Experiment On Incremental Analysis Using Robust Parsing Techniques
Kilian Foth | Wolfgang Menzel | Horia F. Pop | Ingo Schroder
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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A Transformation-based Parsing Technique With Anytime Properties
Kilian Foth | Ingo Schröder | Wolfgang Menzel
Proceedings of the Sixth International Workshop on Parsing Technologies

A transformation-based approach to robust parsing is presented, which achieves a strictly monotonic improvement of its current best hypothesis by repeatedly applying local repair steps to a complex multi-level representation. The transformation process is guided by scores derived from weighted constraints. Besides being interruptible, the procedure exhibits a performance profile typical for anytime procedures and holds great promise for the implementation of time-adaptive behaviour.

1998

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Eliminative Parsing with Graded Constraints
Johannes Heinecke | Jurgen Kunze | Wolfgang Menzel | Ingo Schroder
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Eliminative Parsing with Graded Constraints
Johannes Heinecke | Jurgen Kunze | Wolfgang Menzel | Ingo Schroder
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Decision Procedures for Dependency Parsing Using Graded Constraints
Wolfgang Menzel | Ingo Schroder
Processing of Dependency-Based Grammars

1990

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Anticipation-Free Diagnosis of Structural Faults
Wolfgang Menzel
COLING 1990 Volume 3: Papers presented to the 13th International Conference on Computational Linguistics

1988

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Error Diagnosing and Selection in a Training System for Second Language Learning
Wolfgang Menzel
Coling Budapest 1988 Volume 2: International Conference on Computational Linguistics

1987

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Automated Reasoning About Natural Language Correctness
Wolfgang Menzel
Third Conference of the European Chapter of the Association for Computational Linguistics

1982

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A Procedure of an Automatic Grapheme-to-Phoneme Transfornation of German
Sabine Koch | Wolfgang Menzel | Ingrid Starke
Coling 1982 Abstracts: Proceedings of the Ninth International Conference on Computational Linguistics Abstracts