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1 ---
2 ---
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3 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
4 "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
5
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6 <!-- The two dashed lines at the top of this file ensure that it is
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7 processed by Jekyll; do not remove them.-->
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8 <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en-us">
9 <head>
10 <meta http-equiv="content-type" content="text/html; charset=utf-8" />
11 <title>JHU MT course</title>
12
13 <!-- Homepage CSS -->
14 <link rel="stylesheet" href="screen.css" type="text/css" media="screen, projection" />
15 </head>
16 <body>
17
18 <div class="site">
19
20 <div class="leftsidebar">
21 <p><img src="img/artsrouni.jpg" width="180"
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22 alt="mechanical brain"/><p>
23 <p><i>Georges Artrouni's mechanical brain, a translation device patented in France in 1933.
24 (Image from Corbé by way of <a href="http://www.hutchinsweb.me.uk/IJT-2004.pdf">John Hutchins</a>)</i></p>
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25 <hr/>
26 <p>
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27 <ul>
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28 <li><b>Assignments</b> (<a href="leaderboard.html">Leader board</a>)
29 <ul class="real">
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30 <li><a href="assignment0.html">Assignment 0</a></li>
31 <li><a href="hw1.html">Assignment 1</a></li>
32 <li><a href="hw2.html"><b>Assignment 2</b></a> (due Apr 2, midnight EDT)</li>
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33 </ul>
34 </li>
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35 <li><b><a href="#overview">Overview</a></b></li>
36 <li><b><a href="#grading">Grading</a></b></li>
37 <li><b><a href="#schedule">Schedule</a></b></li>
38 <li><b><a href="#software">Software</a></b></li>
39 <li><b><a href="#data">Data</a></b></li>
40 <li><b><a href="#resources">Resources</a></b></li>
41 </ul>
42 </p>
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43 </div>
44
45 <div class="content">
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46 <a name="#overview"></a><h1>Machine Translation <font color="lightgrey">: 600.468 : Spring 2012</font></h1>
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47 <div id="course" class="cv">
48
49 <p><b>Instructors:</b>
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50 <a href="http://www.cs.jhu.edu/~ccb/">Chris Callison-Burch</a>,
51 <a href="http://www.cs.jhu.edu/~alopez/">Adam Lopez</a>, and
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52 <a href="http://www.cs.jhu.edu/~post/">Matt Post</a>.
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53 </p>
54
55 <p><b>TAs:</b>
56 <a href="http://www.cs.jhu.edu/~jonny/">Jonny Weese</a> and
57 <a href="http://www.cs.jhu.edu/~juri/">Juri Ganitkevitch</a>.
58 </p>
59
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60
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61 <p><b>Time and Place:</b> Tuesdays and Thursdays from 3:00-4:15, Hackerman 320
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62 <p><b>Office Hours:</b> Tuesdays and Thursdays from 4:15 (immediately after class) or by appointment.</p>
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63 <div class="news">
64 <ul class="real">
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65 <li><b><a href="#project">Project Proposals Due on March 13</a></b></li>
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66 <li><b><a href="hw2.html">Homework 2 Due on April 2 (midnight EDT)</a></b></li>
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67 <li><b><a href="https://piazza.com/jhu/spring2012/en600468">Forum for announcements, questions, discussion, etc.</a></b></li>
68 </div>
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69 <p><b>Level</b>: Senior undergraduate or first-year graduate.
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70 <p><b>Course Catalog Description</b>:
71 <a href="http://translate.google.com">Google translate</a> can instantly
72 translate between any pair of over fifty human languages (for instance, from
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73 French to English). How does it do that? Why does it make the errors that it
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74 does? And how can you build something better? Modern translation systems
75 like Google Translate and <a href="http://www.microsofttranslator.com/">Bing Translator</a>
76 <i>learn</i>
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77 how to translate by reading millions of words of already translated text,
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78 and this course will show you how they work. The course covers a diverse set
79 of fundamental building blocks from linguistics, machine learning, algorithms,
80 data structures, and formal language theory, along with their application to
81 a real and difficult problem in artificial intelligence.
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82
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83 <p><b>Textbook:</b>
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84 Some readings will be drawn from <a href="http://www.statmt.org/book/">Statistical Machine Translation</a> (<a href="http://statmt.org/book/errata.html">errata</a>)
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85 by <a href="http://homepages.inf.ed.ac.uk/pkoehn/">Philipp Koehn</a>
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86 You can read it online through the <a href="https://catalyst.library.jhu.edu/catalog/bib_3522360">JHU library</a> or
87 or purchase from <a href="http://www.amazon.com/Statistical-Machine-Translation-Philipp-Koehn/dp/0521874157">Amazon</a>.
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88 A more compact (but therefore less thorough) <a href="http://www.cs.jhu.edu/~alopez/papers/survey.pdf">survey</a> by
89 <a href="http://www.cs.jhu.edu/~alopez">Adam Lopez</a> is available for free. Note that the readings (and therefore the textbook)
90 aren't strictly required; what's required is that you understand the concepts and are able to apply them.</p>
91
92
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93 <p><b>Goals</b>:
94 By the end of the course, you should have a good grasp of what goes into
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95 a building a large-scale natural language processing system, and experience
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96 selecting and applying diverse techniques from computer science to solve
97 real-world problems.
98
99 <p><b>Requirements</b>:
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100 You'll need strong programming skills for homeworks and a final project.
101 Natural language processing (<a href="http://www.cs.jhu.edu/~jason/465/">465</a>)
102 is recommended, but not required.
103
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104
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105 <a name="grading"></a><h2>Course Structure and Grading</h2>
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106 <h3>Homework (4 assignments, 10 points apiece)</h3>
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107 <p>Class meetings will consist mainly of lectures, but the only way to
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108 fully understand all of the problems that you need to solve in building a
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109 language processing system is to go out and build one. To that end, the course
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110 will be evaluated on four <i>competitive</i> homework assignments and a final
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111 project.</p>
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112
113 <p>The goal of each homework assignment will be to build a system the solves
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114 a well-defined subproblem of machine translation. Students will earn a passing
115 grade (7 points) by correctly implementing a standard algorithm that we specify,
116 and additional credit for building the best system according to an objective
117 metric: 6 points for the best system, 5 points for the second best, and so on.
118 To receive an A in the class, you must compete!
119 For each task we will provide a simple baseline, datasets, and metrics. The tasks
120 are:</p>
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121
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122 <ul>
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123 <li><a href="hw1.html"><b>Alignment</b>: given a set of translated documents that are aligned at the sentence level,
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124 identify the words that are translations of each other.</a></li>
125 <li><a href="hw2.html"><b>Decoding</b>: find the most probable translation of a sentence, given a
126 translation model and a new input sentence.</a> (<b>Due Apr 2</b>)</li>
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127 <li><b>Reranking</b>: find the most <i>accurate</i> translation of a sentence,
128 given an input and a list of ranked alternative translations.</li>
129 <li><b>Evaluation</b>: design a metric of translation accuracy that correlates
130 with human judgement..</li>
131 </ul>
132
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133 <h3>In-Class Presentation: Language in Ten Minutes (10 points)</h3>
134 <p>How are you going to build a machine translation system unless you know at
135 least a little bit about language? You will be required to give
136 a short presentation (~10 minutes) on a particular language <i>that you do
137 not speak natively</i>, e.g., Arabic, Chinese, Czech, Hindi, Italian, or Maltese.</p>
138
139 <p>You should prepare three to six slides for your presentation, covering
140 language facts (demographics, location, etc.) important linguistic
141 characteristics (orthography, morphology, syntax) and computational efforts
142 such as resources, tools, papers. For instance, how many entries are there
143 about the language in the <a href="http://www.mt-archive.info">MT
144 Archive?</a> and what are they generally about? Be creative and have fun.
145 Asking for help from native speakers or language experts is great. But you are
146 ultimately responsible for the presentation.</p>
147
148 <p>This assignment was inspired by <a href="http://www.nizarhabash.com/">Nizar Habash</a>.
149 You might want to browse the
150 <a href="https://sites.google.com/site/comse6998machinetranslation/language-in-10-minutes">examples
151 from his class</a>, and his list of recommended resources:
152 <a href="http://www.ethnologue.com/">Ethnologue</a>,
153 <a href="http://www.omniglot.com/">Omniglot</a>,
154 <a href="http://www.aboutworldlanguages.com/">About World Languages</a>, and the
155 <a href="http://www.mt-archive.info/">Machine Translation Archive</a>.</p>
156
157 <p>Presentations will be graded on thoroughness and clarity. What
158 did you learn from your research that was really interesting? Tell us!</p>
159
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160 <a name="project"></a>
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161 <h3>Final Projects (40 points)</h3>
162
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163 <p>The final project will
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164 be designed by the student or groups of students, with guidance from the
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165 instructors. As with the homework assignments, it should be on well-defined
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166 problem with clearly identified input, output, and evalution, and executed
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167 with creativity and depth.</p>
168
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169 <p>Towards the middle of the term you will be required to turn in a brief
170 project proposal (10 points), laying out the problem, your proposed solution, and a
171 plan for implementation and evaluation. Your final project report (20 points) should explain
172 your implementation, evaluation, and analysis, focusing on a single question:
173 What did you learn? The projects will be presented during an interactive poster
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174 session during the final exam period (10 points).</p>
175
176 <p>The project proposal should be 1-2 pages (there is no hard limit, but it
177 will take us longer to give you feedback if your proposal is long or
178 unclear) and must clearly identify:</p>
179
180 <ul class="real">
181 <li>A single question or problem related to machine translation. This should be
182 stated in the first paragraph. We strongly advise including some simple examples
183 to illustrate the question or problem.</li>
184 <li>An outline of the work to be done: how will your project answer the question
185 or attempt to solve the problem? What models and algorithms will you implement?
186 What <a href="#software">software</a> will you use?</li>
187 <li>A description of planned experiments: how will you know if the question was
188 answered or the problem was solved? You should clearly identify input, output,
189 and evaluation strategy.</li>
190 </ul>
191
192 <p>The proposal is a contract. If we give you full credit for it, that means
193 we expect you to implement it and do a good analysis of the results, and we
194 will give you full credit for the entire project if you do. If you turn in a
195 weak proposal, we will give you the opportunity to submit a revised one before
196 moving forward, but the longer you take to define your project, the less time
197 you will have to implement it, so it's in your best interest to take advantage
198 of this early checkpoint.</p>
199
200 <p>Before the proposal is due, you should make an appointment with one of the
201 instructors in order to discuss project ideas; this will enable you to submit
202 a proposal with full confidence that it will be well-received. Before meeting
203 with us, you might want to browse over topics that we'll be covering later in
204 the term, since these might suggest ideas to you. We will however give you
205 fairly wide latitude to choose a topic as long as it's related in some way to
206 translation and is technically interesting, so you should not feel restricted to
207 these topics. We can suggest topics to you in individual meetings if you're
208 stumped, but it will help us to know what your interests and strengths are, so
209 be prepared to tell us what you're curious about.</p>
210
211 <p>Groups projects of any size are permitted, but we will require an amount of
212 work that is linear in group size, so you should take into account the overhead
213 of group coordination when forming groups. Each group should turn in a single
214 proposal identifying all members. All group members will receive the same grade,
215 and you are stuck with your group members once your proposal is finalized: we
216 refuse to adjudicate stories about who did or did not contribute. Choose your
217 partners carefully.
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218
219 <h3>Quizzes (10 points)</h3>
220
221 <p>These are mainly designed to help us understand how well you're following along.</p>
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222
223
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224 <a name="schedule"></a><h2>Tentative Schedule</h2>
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225
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226 <p>Subject to change as the term progresses.</p>
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227 <table>
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228 <tbody>
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229 <tr><th>Date</th><th>Topics</th><th>Lecturer</th><th>Readings (*=graduate level)</th>
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230 </tr>
231
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232 {{ site.posts }}
233 {% for post in site.posts reversed %}
234 <tr>
235 <td>{{ post.date | date: "%b %d" }}
236 </td>
237 <td><ul>
238 <li>{{ post.title }}</li>
239 {% if post.slides %}
240 <li>
241 <a href="slides/JHU_MT_lecture_{{ post.date | date: '%Y-%m-%d' }}.pdf">[pdf]</a>
242 <a href="slides/JHU_MT_lecture_{{ post.date | date: '%Y-%m-%d' }}.key">[keynote]</a>
243 {% if post.otherstuff %}
244 {% for otherstuff in post.otherstuff %}
245 <a href="{{ otherstuff.url }}">[{{ otherstuff.name }}]</a>
246 {% endfor %}
247 {% endif %}
248 </li>
249 {% endif %}
250 {% if post.language-in-10 %}
251 <li>Language in 10: <a href="slides/{{post.language-in-10}}.pdf">{{ post.language-in-10 }}</a></li>
252 {% endif %}
253 </ul>
254 </td>
255 <td>{{ post.lecturer }}</td>
256 <td>
257 {% if post.reading %}
258 <ul>
259 {% for reading in post.reading %}
260 <li>
261 {% if reading.optional %}* {% endif %}
262 {{ reading.author }},
263 {% if reading.url %}
264 <a href="{{ reading.url }}">{{ reading.title }}</a>
265 {% else %}
266 {{ reading.title }}
267 {% endif %}
268 </li>
269 {% endfor %}
270 </ul>
271 {% endif %}
272 </td>
273 </tr>
274 {% endfor %}
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275
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276 </tbody>
277 </table>
278
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279 <a name="software"></a><h2>Software</h2>
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280 State-of-the-art translation algorithms are implemented in a number of
281 open-source projects. The most popular of these are listed below.
282 They are all actively maintained and have significant userbases.
283 You are free to use and extend these tools (or others) in devising your
284 final project.
285 <ul>
286 <li><a href="http://cs.jhu.edu/~ccb/joshua/">Joshua</a>: a translation toolkit for syntax-based translation, developed at Johns Hopkins (Java).</li>
287 <li><a href="http://www.statmt.org/moses/">Moses</a>: a widely-used toolkit implementing most major translation algorithms (C++).</li>
288 <li><a href="http://www.cdec-decoder.org">cdec</a>: a fast decoder for a variety of translation models (C++).</li>
289 <li><a href="http://kheafield.com/code/kenlm/">KenLM</a>: a fast language-modeling toolkit, can be used with the above systems (C++).</li>
290 <li><a href="http://www.speech.sri.com/projects/srilm/">SRI-LM</a>: a widely-used language modeling toolkit with many features, used with the above systems (C++).</li>
291 <li><a href="http://code.google.com/p/giza-pp/">Giza++</a>: a widely-used word alignment toolkit, originally developed at a Johns Hopkins summer workshop (C++).</li>
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292 <li><a href="http://code.google.com/p/berkeleyaligner/">Berkeley Aligner</a>: a robust Java implementation of several innovative alignment algorithms (Java).</li>
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293 </ul>
294
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295 <a name="data"></a><h2>Data</h2>
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296 Modern machine translation systems work by learning from large amounts
297 of data. Many datasets are freely available. You should use whatever data
298 is appropriate to the problem that you decide to work on for your project.
299 <ul>
300 <li><a href="http://www.statmt.org/wmt11/translation-task.html">Machine Translation workshop 2011 shared task data</a>, used in research evaluations (French-English, Spanish-English, Czech-English, Haitian Creole-English).</li>
301 <li><a href="http://langtech.jrc.it/JRC-Acquis.html">JRC-Acquis</a>, legislative text of the European Union (22 European languages).</li>
302 <li><a href="http://statmt.org/europarl/">Europarl</a>, proceedings of the European Parliament (22 European languages).</li>
303 <li><a href="http://www.isi.edu/natural-language/download/hansard/">Canadian Hansards</a>, proceedings of the Canadian Parliament (French and English).</li>
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304 <li><a href="http://opus.lingfil.uu.se/">OPUS</a> is a collection of parallel corpora in a variety of languages and domains. Includes some interesting domains such as film subtitles.
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305 </ul>
306
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307 <a name="resources"></a><h2>Other Resources and Classes</h2>
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308 <ul>
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309 <li>The <a href="http://aclweb.org/anthology-new/">ACL Anthology</a> archives papers published by the Association for Computational Linguistics, which covers a wide variety of topics in natural language processing. It includes many of the classic papers on machine translation.
310 <li>The <a href="http://mt-archive.info/">MT Archive</a> holds historical and modern research papers on machine translation. There is some overlap with the ACL Anthology, but it is focused specifically on machine translation, and also includes many papers from other venues, as well as historical papers..</li>
311 <li><a href="http://homepages.inf.ed.ac.uk/pkoehn/">Philipp Koehn</a> maintains <a href="http://statmt.org/">statmt.org</a>, with pointers to various resources.
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312 <li><a href="http://www.cs.jhu.edu/~jason/">Jason Eisner</a>'s <a href="http://www.cs.jhu.edu/~jason/465/">natural language processing class</a> at JHU.
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313 <li><a href="http://www.cs.jhu.edu/~mdredze/">Mark Dredze</a>'s <a href="http://www.cs.jhu.edu/~mdredze/teaching/2011_600_475/">machine learning class</a> at JHU.
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314 <li>Chris Callison-Burch taught a one-week machine translation course at ESSLLI 2005 with <a href="http://homepages.inf.ed.ac.uk/pkoehn/">Philipp Koehn</a>:
315 <a href="http://homepages.inf.ed.ac.uk/pkoehn/publications/esslli-slides-day1.pdf">1</a>,
316 <a href="http://homepages.inf.ed.ac.uk/pkoehn/publications/esslli-slides-day2.pdf">2</a>,
317 <a href="http://homepages.inf.ed.ac.uk/pkoehn/publications/esslli-slides-day3.pdf">3</a>,
318 <a href="http://homepages.inf.ed.ac.uk/pkoehn/publications/esslli-slides-day4.pdf">4</a>,
319 <a href="http://homepages.inf.ed.ac.uk/pkoehn/publications/esslli-slides-day5.pdf">5</a>.</li>
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320 <li>Adam Lopez taught a one-week <a href="http://www.cs.jhu.edu/~alopez/esslli2010.html">machine translation course</a> at ESSLLI 2010.</li>
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321 <li>Machine translation course at <a href="http://nlg.isi.edu/teaching/cs599mt/">University of Southern California</a>.</li>
322 <li>Machine translation course at <a href="http://www.inf.ed.ac.uk/teaching/courses/mt/">University of Edinburgh</a>.</li>
323 <li>Machine translation course at <a href="https://catalyst.uw.edu/workspace/kristout/20547/123745">University of Washington</a>.</li>
324 <li>Machine translation course at <a href="http://www.cs.cmu.edu/afs/cs/project/cmt-55/lti/Courses/731/www/Spring-11/ClassSchedule2011.htm">Carnegie Mellon University</a>.</li>
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325 <li>Machine translation course at <a href="https://sites.google.com/site/comse6998machinetranslation/">Columbia University</a>.</li>
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326 <li>Machine translation course at <a href="http://www.cs.sfu.ca/~anoop/teaching/CMPT-882-Fall-2011/">Simon Fraser University</a>.</li>
327 <li>A course in advanced topics in machine translation at <a href="http://www.cs.cmu.edu/afs/cs/project/cmt-55/lti/Courses/734/www/">Carnegie Mellon University</a> runs concurrently with this class.</li>
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328 </ul>
329 </div>
330
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331 <div class="footer">
332 <p>Last updated on {{ site.time | date: "%B %d, %Y" }}. Site created using
333 <a href="http://git-scm.com/">git</a>,
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334 <a href="https://github.com/mojombo/jekyll">jekyll</a>,
296baf7 @alopez Adds github credit
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335 and <a href="http://www.vim.org/">vim</a>, and hosted on <a href="https://github.com/">github</a>.</p>
7ce3f4d @alopez Added CC license notice and credits
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336 <p><a rel="license" href="http://creativecommons.org/licenses/by/3.0/">
337 <img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by/3.0/80x15.png" /></a><br />
338 Except where noted, the lectures, assignments, and other material hosted on this page were created by
339 <span xmlns:cc="http://creativecommons.org/ns#" property="cc:attributionName">Adam Lopez, Chris Callison-Burch, and Matt Post</span> and are licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution 3.0 Unported License</a>.
30127f3 @alopez Fix typo in footer
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340 That means you're free to reuse the
b03abb4 @alopez Fixed URLs after running link checker
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341 <a href="https://github.com/mt-class/mt-class.github.com">source code</a>, though please acknowledge that you got it from us. Thanks!</p>
7ce3f4d @alopez Added CC license notice and credits
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342 </div>
343
958f969 @alopez First draft of course page
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344 </body>
345 </html>
e38f077 @mjpost test change
mjpost authored
346
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