- Bachelor students: Einführung in das wissenschaftliche Arbeiten (Proseminar)
- Master students: Bachelor degree
- Diploma students: Vordiplom
- Attendance of at least one of the lectures Pattern Recognition and Neural Networks, Introduction to Statistical Classification, Automatic Speech Recognition, or Statistical Methods in Natural Language Processing, or evidence of equivalent knowledge.
- For successful participants of the above lectures, the possibility of a seminar talk is guaranteed.

The presentation block is scheduled for

incl. lunch breaks. The order of the presentations will be as shown here. Nevertheless, to be able to accomodate changes in the presentation order if necessary on short notice, we ask all participants to be prepared by the first day of the presentation block and have their slides ready.

initial proposals (report's content page) have to be submitted by email to your supervisors by Oct. 6, 2014. At this time participants must arrange an appointment with the individual supervisor.__Proposals:__must be submitted at least 1 month prior to the trial presentation date, but__Article:__**not later than Nov. 17, 2014**to the individual supervisor in electronic form (PDF).

must be submitted at least 1 week prior to the trial presentation date to the individual supervisor in electronic form (PDF).__Presentation slides:__at least 2 weeks prior to the actual presentation date. Please refer to your individual supervisor to schedule your trial presentation.__Trial presentations:__will be scheduled after the kick-off meeting, see comments above on seminar mode.__Seminar presentations:__must be submitted 2 weeks after the presentation date at the latest to the individual supervisor in electronic form (PDF).__Final (corrected) articles and presentation slides:__in order to receive a certificate participants must attend all presentation sessions.__Compulsory attendance:__The Computer Science Department of RWTH Aachen University has adopted ethical guidelines for the authoring of academic work such as seminar reports. Each student has to comply with these guidelines. In this regard, you, as a seminar attendant, have to sign a declaration of compliance, in which you assert that your work complies with the guidelines, that all references used are properly cited, and that the report was done autonomously by yourself. We ask you do download the guidelines and submit the declaration together with your seminar report and talk to your individual supervisor. You also find a German version of the guidelines and a German version of the declaration you may use as well.__Ethical Guidelines:__

*Note: failure to meet deadlines, absence without
permission from compulsory sessions (presentations and
preliminary meeting as announced by email to each
participating student), or dropping out of the seminar after
more than 3 weeks after the kick-off meeting
(i.e. by Aug. 29, 2014)
results in the grade 5.0/not appeared.
*

**Machine Translation Evaluation**(NN; Supervisor: Markus Freitag)

References:

- K. Papineni, S. Roukus, T. Ward and W. Zhu: "BLEU: a Method for Automatic Evaluation of Machine Translation," in
*Proc. ACL*, pp. 311-318, Philadelphia, PA, July 2002. - M. Snover, B. Dorr, R. Schwartz, L. Micciulla, and J. Makhoul: "A Study of Translation Edit Rate with Targeted Human Annotation," in
*Proc. AMTA*, pp. 223-231. August 2006. - A. Lavie, and A. Agarwal: "METEOR: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments," in
*Proc. WMT*, pp. 228-231, Prague, Czech Republic, June 2007.

- K. Papineni, S. Roukus, T. Ward and W. Zhu: "BLEU: a Method for Automatic Evaluation of Machine Translation," in
**IBM Translation Models**(NN; Supervisor: Andy Guta)

References:

- Chapter 4 of P. Koehn: "Statistical Machine Translation," textbook,
*Cambridge University Press*, January 2010. - F. Och, and H. Ney: "Improved statistical alignment models," in
*Proc. ACL*, pp. 440-447, Hong Kong, 2000.

- Chapter 4 of P. Koehn: "Statistical Machine Translation," textbook,
**Decoding for Phrase-Based Statistical Machine Translation**(NN; Supervisor: Joern Wübker)

References:

- Chapter 6 of P. Koehn: "Statistical Machine Translation," textbook,
*Cambridge University Press*, January 2010. - P. Koehn: "Pharaoh: a Beam Search Decoder for Phrase-Based Statistical Machine Translation Models," in
*Proc. AMTA*, Washington, DC, September 2004.

- Chapter 6 of P. Koehn: "Statistical Machine Translation," textbook,
**Hierarchical Phrase-based Machine Translation**(Timmermanns; Supervisor: Stephan Peitz)

References:

- D. Chiang: "Hierarchical Phrase-Based Translation,"
*Computational Linguistics*, Vol. 33(2), pp 201-228, June 2007.

- D. Chiang: "Hierarchical Phrase-Based Translation,"
**N-gram-based Machine Translation**(NN; Supervisor: Andy Guta)

References:

- J, Mariño, R. Banchs, J. Crego, A. Gispert, P. Lambert, J. Fonollosa, and M. Costa-jussà: "N-gram-based Machine Translation," in
*Computational Linguistics*, Vol. 32(4), pp. 527-549, December 2006.

- J, Mariño, R. Banchs, J. Crego, A. Gispert, P. Lambert, J. Fonollosa, and M. Costa-jussà: "N-gram-based Machine Translation," in
**Lightly-Supervised Training for Statistical Machine Translation**(Hamadache; Supervisor: Malte Nuhn)

References:

- H. Schwenk: "Investigations on Large-Scale Lightly-Supervised Training for Statistical Machine Translation," in
*Proc. IWSLT*, pp. 182-189, Waikiki, Hawaii, USA, October 2008.

- H. Schwenk: "Investigations on Large-Scale Lightly-Supervised Training for Statistical Machine Translation," in
**Large Scale Parallel Document Mining for Machine Translation**(Frohn; Supervisor: Malte Nuhn)

References:

- J. Uszkoreit, J. Ponte, A. Popat, and M. Dubiner: "Large Scale Parallel Document Mining for Machine Translation," in
*Proc. COLING*, pp. 1101-1109, Beijing, China, 2010. - J. Smith, H. Saint-Amand, M. Plamada, P. Koehn, C. Callison-Burch, and A. Lopez: "Dirt Cheap Web-Scale Parallel Text from the Common Crawl," in
*Proc. ACL*, Sofia, Bulgaria, August 2013.

- J. Uszkoreit, J. Ponte, A. Popat, and M. Dubiner: "Large Scale Parallel Document Mining for Machine Translation," in
**System Combination for Machine Translation**(Wallraff; Supervisor: Markus Freitag)

References:

- E. Matusov et al.: "System Combination for Machine Translation of Spoken and Written Language,"
*IEEE Transactions on Audio, Speech and Language Processing*, Vol. 16(7), pp. 1222-1237, September 2008. - A. V. Rosti, N. F. Ayan, B. Xiang, S. Matsoukas, R. Schwartz, and B. Dorr: "Combining Outputs from Multiple Machine Translation Systems," in
*Proc. NAACL*, Rochester, NY, USA, April 2007.

- E. Matusov et al.: "System Combination for Machine Translation of Spoken and Written Language,"
**Deciphering Foreign Language**(Richter; Supervisor: Malte Nuhn)

References:

- S. Ravi, and K. Knight: "Deciphering Foreign Language", in
*Proc. ACL*, pp. 12-21, Portland, Oregon, USA, 2011.

- S. Ravi, and K. Knight: "Deciphering Foreign Language", in

**Operation Sequence Model**(Graça; Supervisor: Andy Guta)

References:

- N. Durrani, A. Fraser, H. Schmid, and H. Hoang: "Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?," in
*Proc. ACL*, Sofia, Bulgaria, August 2013. - N. Durrani, A. Fraser, and H. Schmid: "Model With Minimal Translation Units, But Decode With Phrases," in
*Proc. NAACL*, Atlanta, Georgia, USA, June 2013.

- N. Durrani, A. Fraser, H. Schmid, and H. Hoang: "Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?," in
**Discriminative Training for MT**(Vaitl; Supervisor: Joern Wübker)

References:

- S. Green, S. Wang, D. Cer, and C. D. Manning: "Fast and adaptive online training of feature-rich translation models," in
*Proc. ACL*, pp. 311–321, Sofia, Bulgaria, August 2013. - H. Yu, L. Huang, H. Mi, and K. Zhao: "Max-violation perceptron and forced decoding for scalable mt training," in
*Proc. EMNLP*, pp. 1112–1123, Seattle, USA, October 2013.

- S. Green, S. Wang, D. Cer, and C. D. Manning: "Fast and adaptive online training of feature-rich translation models," in
**Word Alignment with Neural Network**(Soliman; Supervisor: Jan-Thorsten Peter)

References:

- N. Yang, S. Liu, M. Li, M. Zhou, and N. Yu: "Word Alignment Modeling with Context Dependent Deep Neural Network," in
*Proc. ACL*, Sofia, Bulgaria, August 2013.

- N. Yang, S. Liu, M. Li, M. Zhou, and N. Yu: "Word Alignment Modeling with Context Dependent Deep Neural Network," in
**Neural Network in Decoding**(Rossenbach; Supervisor: Jan-Thorsten Peter)

References:

- J. Devlin, R. Zbib, Z. Huang, T. Lamar, R. Schwartz and J. Makhoul: "Fast and Robust Neural Network Joint Models for Statistical Machine Translation," in
*Proc. ACL*, Baltimore, Maryland, USA, June 2014. - A. Vaswani, Y. Zhao, V. Fossum, and D. Chiang: "Decoding with Large-Scale Neural Language Models Improves Translation," in
*Proc. EMNLP*, Seattle, USA, October 2013.

- J. Devlin, R. Zbib, Z. Huang, T. Lamar, R. Schwartz and J. Makhoul: "Fast and Robust Neural Network Joint Models for Statistical Machine Translation," in
**Recurrent Neural Networks for Translation Modelling**(NN; Supervisor: Joern Wübker)

References:

- S. Liu, N. Yang, M. Li, and M. Zhou: "A Recursive Recurrent Neural Network for Statistical Machine Translation," in
*Proc. ACL*, pp. 1491-1500, Baltimore, Maryland, USA, June 2014 - N. Kalchbrenner, P. Blunsom: "Recurrent Continuous Translation Models,"
in
*Proc. EMNLP*, pp. 1700-1709, Seattle, WA, Oct. 2013.

- S. Liu, N. Yang, M. Li, and M. Zhou: "A Recursive Recurrent Neural Network for Statistical Machine Translation," in
**Syntax-based Machine Translation - String To Tree**(Bretschner; Supervisor: Stephan Peitz)

References:

- P. Williams, and P. Koehn: "GHKM rule extraction and scope-3 parsing in Moses," in
*Proc. WMT*, pp. 388-394, Montreal, Quebec, Canada, June 2012. - M. Galley, M. Hopkins, K. Knight, and D. Marcu: "What's in a translation rule?", in
*Proc. NAACL*, pp. 273-280, Boston, Massachusetts, USA, May 2004.

- P. Williams, and P. Koehn: "GHKM rule extraction and scope-3 parsing in Moses," in
**Syntax-based Machine Translation - Tree To String**(Schupp; Supervisor: Stephan Peitz)

References:

- G. Neubig, and K. Duh: "On the Elements of an Accurate Tree-to-String Machine Translation System," in
*Proc. ACL*, Baltimore, Maryland, USA, June 2014. - Y. Liu, Q. Liu, and S. Lin: "Tree-to-string alignment template for statistical machine translation," in
*Proc. ACL*, pp. 609-616, Sydney, Australia, July 2006.

- G. Neubig, and K. Duh: "On the Elements of an Accurate Tree-to-String Machine Translation System," in

- Online LaTeX-Documentation:

- Document Templates:

- Article Template (164kB), contains the template and all necessary files in tar format (or here 53kB in zip format).
- Presentation Slide Template (4.9MB), a zip file containing the template and all necessary graphics as well as the institute’s style template.

- Guidelines for articles and presentation slides:

- The aim of the seminar for the participants is to learn the following:
- to tackle a topic and to expand knowledge
- to critically analyze the literature

- to hold a presentation

- Take notice of references
to other topics in the seminar and discuss topics with one
another!

- Take care to stay within your
own topic. To this end participants should be aware of the other
topics in the seminar. If applicable, cross-reference
other articles and presentations.

*Important*: As part of the introduction, a slide should outline the most important literature used for the presentation. In addition, the presentation should clearly indicate which literature the particular elements of the presentation refer to.

- Take notice of references
to other topics in the seminar and discuss topics with one
another!

- Participants are expected to seek out additional literature on their topic. Assistance with the literature search is available at the faculty’s library. Access to literature is naturally also available at the Lehrstuhl Informatik 6 library.
- Notation/Mathematical
Formulas: consistent, correct notation
is essential. When necessary, differing notation from various
literature sources is to be modified or standardized in order to be
clear and consistent. The
lectures held by the Lehrstuhl Informatik 6 should provide a
guide as to what appropriate notation should look like.

- Tables must have titles (appearing above the table).
- Figures
must have captions (appearing below the figure).

- In the case that no adequate translation of an English technical term is available, the term should be used unchanged.
- Articles and presentation slides can also be prepared in
English.

- Completeness:
acknowledge all literature and
sources.

- Referencing must conform to the standard described in the article template.
- Examples should be used to illustrate points.
**Examples**should be as complex as necessary but as simple as possible.- Slides should be used as presentation aids and not to replace the role of the presenter; specifically, slides should:
- illustrate important points and relationships;
- remind the audience (and the presenter) of important aspects
and considerations;

- give the audience an overview
of the presentation.

- Slides should not contain chunks of text or complicated sentences; rather they should consist of succinct words and terms.
- Use illustrations where appropriate - a picture says a thousand words!
**Abbreviations**should be defined at the first usage in the manner demonstrated in the following example:

- Take care to stay within your own topic. To this end participants should be aware of the other topics in the seminar. If applicable, cross-reference other articles and presentations.
- Usage of fonts,
typefaces and colors in presentation slides must be
consistent and appropriate. Such means should serve to
clarify points or relationships, not be applied needlessly
or at random.

- Care should be taken when selecting fonts for presentation slides (also within diagrams) to ensure legibility on a projector even for those seated far from the screen.

Dr. Ralf Schlüter

RWTH Aachen

Lehrstuhl Informatik 6

Ahornstr. 55

52056 Aachen
Raum 6125b

Telefon: 0241 / 80-21612

E-Mail: schlueter@cs.rwth-aachen.de