Markus Saers


2016

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Learning Translations for Tagged Words: Extending the Translation Lexicon of an ITG for Low Resource Languages
Markus Saers | Dekai Wu
Proceedings of the Workshop on Multilingual and Cross-lingual Methods in NLP

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Improving word alignment for low resource languages using English monolingual SRL
Meriem Beloucif | Markus Saers | Dekai Wu
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)

We introduce a new statistical machine translation approach specifically geared to learning translation from low resource languages, that exploits monolingual English semantic parsing to bias inversion transduction grammar (ITG) induction. We show that in contrast to conventional statistical machine translation (SMT) training methods, which rely heavily on phrase memorization, our approach focuses on learning bilingual correlations that help translating low resource languages, by using the output language semantic structure to further narrow down ITG constraints. This approach is motivated by previous research which has shown that injecting a semantic frame based objective function while training SMT models improves the translation quality. We show that including a monolingual semantic objective function during the learning of the translation model leads towards a semantically driven alignment which is more efficient than simply tuning loglinear mixture weights against a semantic frame based evaluation metric in the final stage of statistical machine translation training. We test our approach with three different language pairs and demonstrate that our model biases the learning towards more semantically correct alignments. Both GIZA++ and ITG based techniques fail to capture meaningful bilingual constituents, which is required when trying to learn translation models for low resource languages. In contrast, our proposed model not only improve translation by injecting a monolingual objective function to learn bilingual correlations during early training of the translation model, but also helps to learn more meaningful correlations with a relatively small data set, leading to a better alignment compared to either conventional ITG or traditional GIZA++ based approaches.

2015

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Improving semantic SMT via soft semantic role label constraints on ITG alignmens
Meriem Beloucif | Markus Saers | Dekai Wu
Proceedings of Machine Translation Summit XV: Papers

2014

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Better Semantic Frame Based MT Evaluation via Inversion Transduction Grammars
Dekai Wu | Chi-kiu Lo | Meriem Beloucif | Markus Saers
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Ternary Segmentation for Improving Search in Top-down Induction of Segmental ITGs
Markus Saers | Dekai Wu
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Lexical Access Preference and Constraint Strategies for Improving Multiword Expression Association within Semantic MT Evaluation
Dekai Wu | Chi-kiu Lo | Markus Saers
Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex)

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XMEANT: Better semantic MT evaluation without reference translations
Chi-kiu Lo | Meriem Beloucif | Markus Saers | Dekai Wu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Learning to Freestyle: Hip Hop Challenge-Response Induction via Transduction Rule Segmentation
Dekai Wu | Karteek Addanki | Markus Saers | Meriem Beloucif
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Bayesian Induction of Bracketing Inversion Transduction Grammars
Markus Saers | Dekai Wu
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Combining Top-down and Bottom-up Search for Unsupervised Induction of Transduction Grammars
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of the Seventh Workshop on Syntax, Semantics and Structure in Statistical Translation

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Unsupervised Transduction Grammar Induction via Minimum Description Length
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of the Second Workshop on Hybrid Approaches to Translation

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Unsupervised Learning of Bilingual Categories in Inversion Transduction Grammar Induction
Markus Saers | Dekai Wu
Proceedings of the 13th International Conference on Parsing Technologies (IWPT 2013)

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Unsupervised learning of bilingual categories in inversion transduction grammar induction
Markus Saers | Dekai Wu
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

We present the first known experiments incorporating unsupervised bilingual nonterminal category learning within end-to-end fully unsupervised transduction grammar induction using matched training and testing models. Despite steady recent progress, such induction experiments until now have not allowed for learning differentiated nonterminal categories. We divide the learning into two stages: (1) a bootstrap stage that generates a large set of categorized short transduction rule hypotheses, and (2) a minimum conditional description length stage that simultaneously prunes away less useful short rule hypotheses, while also iteratively segmenting full sentence pairs into useful longer categorized transduction rules. We show that the second stage works better when the rule hypotheses have categories than when they do not, and that the proposed conditional description length approach combines the rules hypothesized by the two stages better than a mixture model does. We also show that the compact model learned during the second stage can be further improved by combining the result of different iterations in a mixture model. In total, we see a jump in BLEU score, from 17.53 for a standalone minimum description length baseline with no category learning, to 20.93 when incorporating category induction on a Chinese–English translation task.

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Improving machine translation by training against an automatic semantic frame based evaluation metric
Chi-kiu Lo | Karteek Addanki | Markus Saers | Dekai Wu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Segmenting vs. Chunking Rules: Unsupervised ITG Induction via Minimum Conditional Description Length
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Modeling Hip Hop Challenge-Response Lyrics as Machine Translation
Karteek Addanki | Markus Saers | Dekai Wu
Proceedings of Machine Translation Summit XIV: Papers

2012

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From Finite-State to Inversion Transductions: Toward Unsupervised Bilingual Grammar Induction
Markus Saers | Karteek Addanki | Dekai Wu
Proceedings of COLING 2012

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LTG vs. ITG Coverage of Cross-Lingual Verb Frame Alternations
Karteek Addanki | Chi-kiu Lo | Markus Saers | Dekai Wu
Proceedings of the 16th Annual conference of the European Association for Machine Translation

2011

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Reestimation of Reified Rules in Semiring Parsing and Biparsing
Markus Saers | Dekai Wu
Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation

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The Uppsala-FBK systems at WMT 2011
Christian Hardmeier | Jörg Tiedemann | Markus Saers | Marcello Federico | Prashant Mathur
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Principled Induction of Phrasal Bilexica
Markus Saers | Dekai Wu
Proceedings of the 15th Annual conference of the European Association for Machine Translation

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On the Expressivity of Linear Transductions
Markus Saers | Dekai Wu | Chris Quirk
Proceedings of Machine Translation Summit XIII: Papers

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Linear Transduction Grammars and Zipper Finite-State Transducers
Markus Saers | Dekai Wu
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Word Alignment with Stochastic Bracketing Linear Inversion Transduction Grammar
Markus Saers | Joakim Nivre | Dekai Wu
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Linear Inversion Transduction Grammar Alignments as a Second Translation Path
Markus Saers | Joakim Nivre | Dekai Wu
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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A Systematic Comparison between Inversion Transduction Grammar and Linear Transduction Grammar for Word Alignment
Markus Saers | Joakim Nivre | Dekai Wu
Proceedings of the 4th Workshop on Syntax and Structure in Statistical Translation

2009

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Improving Phrase-Based Translation via Word Alignments from Stochastic Inversion Transduction Grammars
Markus Saers | Dekai Wu
Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) at NAACL HLT 2009

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Learning Stochastic Bracketing Inversion Transduction Grammars with a Cubic Time Biparsing Algorithm
Markus Saers | Joakim Nivre | Dekai Wu
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

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Mixing and Blending Syntactic and Semantic Dependencies
Yvonne Samuelsson | Oscar Täckström | Sumithra Velupillai | Johan Eklund | Mark Fishel | Markus Saers
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

2007

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Single Malt or Blended? A Study in Multilingual Parser Optimization
Johan Hall | Jens Nilsson | Joakim Nivre | Gülşen Eryiǧit | Beáta Megyesi | Mattias Nilsson | Markus Saers
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)