Main characteristics of a translation task according to the FEMTI report (Q3)
Main characteristics of a translation task according to the FEMTI report
"The Framework for Machine Translation Evaluation in ISLE is a resource that helps MT evaluators define contextual evaluation plans. FEMTI consists of two interrelated classifications or taxonomies: the first one lists possible characteristics of the contexts of use that are applicable to MT systems. The second one lists the possible characteristics of an MT system, along with the metrics that were proposed to measure them".
The characteristics of the translation task are the follow:
- Assimilation: “The ultimate purpose of the assimilation task (of which translation forms a part) is to monitor a (relatively) large volume of texts produced by people outside the organization, in (usually) several languages.”
- Dissemination: “The ultimate purpose of dissemination is to deliver to others a translation of documents produced inside the organization.”
- Communication: “The ultimate purpose of the communication task is to support multi-turn dialogues between people who speak different languages. The translation quality must be high enough for painless conversation, despite possible syntactically ill-formed input and idiosyncratic word and format usage. The ultimate purpose of dissemination is to deliver to others a translation of documents produced inside the organization.”
Zabaldu
Bildu
TwitterTranslation examples by MT systems (Q3)
Translation examples by MT systems
"Machine translation, sometimes referred to by the acronym MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of words in one natural language for words in another. Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies".
These are some examples of MT systems:
- OpenTrad. (2005). Traducción automática en código abierto.
- http://www.opentrad.org/demo/
- Instituto Cervantes. Servicio de traducción automática interactivo.
- http://oesi.cervantes.es/traduccionAutomatica.html
- Reverso translate on line. (2007). El Mundo.
- http://elmundo.reverso.net/textonly/default.asp
- Comprendium Translator. (2007). Traductor automático.
- http://www.translendium.net:8080/home/
International meetings on Computational Linguistics (Q2)
International meetings on Computational Linguistics
Complete the references and make a general comment on the following international meetings on Computational Linguistics
These are some of the conferences that will take place this year:
- 45th Annual Meeting of the Asocciation for computational Linguistics
These are the demos program of the conference:
Session 1
- Demo Proposal for MIMUS: A Multimodal and Multilingual Dialogue System for the Home Domain
J. Gabriel Amores, Guillermo Pérez and Pilar Manchón - A Translation Aid System with a Stratified Lookup Interface
Takeshi Abekawa and Kyo Kageura - Multimedia Blog Creation System using Dialogue with Intelligent Robot
Akitoshi Okumura, Takahiro Ikeda, Toshihiro Nishizawa, Shin-ichi Ando and Fumihiro Adachi - SemTAG: a platform for specifying Tree Adjoining Grammars and performing TAG-based Semantic Construction
Claire Gardent and Yannick Parmentier - System Demonstration of On-Demand Information Extraction
Akira Oda and Satoshi Sekine - Multilingual Ontological Analysis of European Directives
Gianmaria Ajani, Guido Boella, Leonardo Lesmo, Alessandro Mazzei and Piercarlo Rossi
Session 2
- zipfR: Word Frequency Modeling in R
Stefan Evert and Marco Baroni - Linguistically Motivated Large-Scale NLP with C&C and Boxer
James Curran, Stephen Clark and Johan Bos - Don't worry about metaphor: affect detection for conversational agents
Catherine Smith, Timothy Rumbell, John Barnden, Robert Hendley, Mark Lee, Alan Wallington and Li Zhang - An efficient algorithm for building a distributional thesaurus (and other Sketch Engine developments)
Pavel Rychly and Adam Kilgarriff - Semantic enrichment of journal articles using chemical NER
Colin R. Batchelor and Peter T. Corbett - An API for Measuring the Relatedness of Words in Wikipedia
Simone Paolo Ponzetto and Michael Strube - NICT-ATR Speech-to-Speech Translation System
Eiichiro Sumita, Tohru Shimizu and Satoshi Nakamura
http://ufal.mff.cuni.cz/acl2007/
- Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007)
Demos
- The Automated Text Adaptation Tool
- Jill Burstein, Jane Shore, John Sabatini, Yong-Won Lee, Matthew Ventura
- TextRunner: Open Information Extraction on the Web
- Alexander Yates, Michele Banko, Matthew Broadhead, Michael Cafarella, Oren Etzioni, Stephen Soderland
- Text Comparison Using Machine-Generated Nuggets
- Liang Zhou
- Voice-Rate: A Dialog System for Consumer Ratings
- Geoffrey Zweig, Y.C. Ju, Patrick Nguyen, Dong Yu, Ye-Yi Wang and Alex Acero
- OMS-J: An Opinion Mining System for JapaneseWeblog Reviews Using a Combination of Supervised and Unsupervised Approaches
- Guangwei Wang, Kenji Araki
- The Hidden Information State Dialogue Manager: A Real-World POMDP-Based System
- Steve Young, Jost Schatzmann, Blaise Thomson, Karl Weilhammer, Hui Ye
- Cedit: Semantic Networks Manual Annotation Tool
- Václav Novák
- POSSLT: A Korean to English Spoken Language Translation System
- Donghyeon Lee, Jonghoon Lee, Gary Geunbae Lee
- Adaptive Tutorial Dialogue Systems Using Deep NLP Techniques
- Myroslava O. Dzikovska, Charles B. Callaway, Elaine Farrow, Manuel Marques-Pita, Colin Matheson, Johanna D. Moore
- Automatic Segmentation and Summarization of Meeting Speech
- Pei-Yun (Sabrina) Hsueh, Gabriel Murray, Simon Tucker, Jonathan Kilgour, Jean Carletta, Johanna Moore, Steve Renals
- Spoken Dialogue Systems for Language Learning
- Stephanie Seneff, Chao Wang, Chih-yu Chao
- RavenCalendar: A Multimodal Dialog System for Managing A Personal Calendar
- Svetlana Stenchikova, Basia Mucha, Sarah Hoffman, Amanda Stent
- Learning to find transliteration on the Web
- Chien-Cheng Wu, Jason S. Chang
- The CALO Meeting Assistant Demo
- Lynn Voss, Patrick Ehlen, and the CALO Meeting Assistant Project Team
- Demonstration of PLOW: A Dialogue System for One-Shot Task Learning
- James Allen, Nathanael Chambers, George Ferguson, Lucian Galescu, Hyuckchul Jung, Mary Swift, William Taysom
- A Conversational In-car Dialog System
- Baoshi Yan, Fuliang Weng, Zhe Feng, Florin Ratiu, Madhuri Raya, Yao Meng, Sebastian Varges, Matthew Purver, Annie Lien, Tobias Scheideck, Badri Raghunathan, Feng Lin, Rohit Mishra, Brian Lathrop, Zhaoxia Zhang, Harry Bratt, Stanley Peters
Definitions of Human Language Technologies (Q1)
Definitions of Human Language Technologies:
Look for two or more definitions of Human Language Technologies by scholars or relevant sites on the Web. Please, quote the authors properly.
- "Human Language Technologiy (HTL) makes it easier for people to interact with machines. This can benefit a wide range of people - from illiterate farmers in remote villages who want to obtain relevant medical information over a cellphone, to scientist in state-of-the-art laboratories who want to focus on problem-solving with computers".
www.meraka.org.za/humanLanguage.htm
- "Language technology — sometimes also referred to as human language technology — comprises computational methods, computer programs and electronic devices that are specialized for analyzing, producing or modifying texts and speech. These systems must be based on some knowledge of human language. Therefore language technology defines the engineering branch of computational linguistics".
Reasons to study Human Language Technologies (Q2)
Reasons to study Human Language Technologies
Note down and discuss five reasons to study Human Language Technologies. I will give you one: ScholarShips are available on this topic
These are the two different reasons I have found:
- "HLTC is a multidisciplinary research center at the Hong Kong University of Science and Technology (HKUST) whose mission is to lead state-of-the-art research directions that drive the development of new applications in both text and spoken language technology. HLTC is led by seven faculty members from the EEE and the CS departments: Oscar Au, Roland Chin, Pascale Fung, Brian Mak, Bertram Shi, Manhung Siu, and Dekai Wu, specializing in speech and signal processing, statistical and corpus-based natural language processing, machine translation, text mining, information extraction, Chinese language processing, knowledge management, and related fields. Special emphasis is given to machine processing of Chinese language and Chinese information. Systems built at HLTC include automated language translation for the Internet, speech-based web browsing, and speech recognition for the telephone".
http://littera.deusto.es/prof/abaitua/hlt/hlt0607/ScholarShips
- "The capabilities of human language technology (HLT) have grown substantially in recent years, both in the research laboratory and in the commercial marketplace. There is now a wide range of applications for HLT systems such as automatic transcription of meetings, translation between languages (e.g. Arabic and English), automatic answering of questions, text mining (e.g. from the web) and access to information through spoken human-computer dialogue. Systems which use HLT are now in everyday use, through technologies such as internet search engines and mobile phones, and most major international computer and telecoms companies now engage in HLT research and development. As a result, there is strong demand for graduates with the highly-specialised multi-disciplinary skills that are required in HLT, both as practitioners in the development of HLT applications and as researchers into the advanced capabilities required for next-generation HLT systems".
