Contact: Janice Campbell, Alex Yanishevsky (firstname.lastname@example.org)
The Commercial Users and Translators track will focus on how MT helps individual translators, Language Service Providers, and enterprise users deliver their products and services more effectively. Submissions should report on the use of MT to support business goals and serve customer needs in commercial settings by integrating it with other processes and technologies.
Theme – Applying Innovation to Business Challenges
Producing ever-increasing volumes of multilingual content while keeping costs in check has become a mantra for businesses poised for growth. These challenges are being met through the adoption of innovative technologies and tools, the automation of processes and workflows, and the application of artificial intelligence approaches.
The machine translation technology landscape is dotted with innovations in translation productivity tools, advancements in neural networks, and novel approaches to producing and delivering content to international audiences.
The goal of the commercial track is to provide a broad spectrum of machine translation applications to achieve the rapid delivery of multilingual content within the constraints of time, cost, scope and quality.
Topics of interest include, but are not limited to, the following:
- PBSMT vs. NMT: considerations on cost, quality, maintenance and fit for purpose.
- Adaptive and interactive MT tools.
- Approaches for automated language variety converters, such as US to UK English, Brazilian to Continental Portuguese.
- Approaches for related language pair converters, such Russian < > Ukrainian, Croatian < > Serbian, Spanish < > Portuguese.
- MT Quality evaluation scores, tools, and metrics that support business KPIs.
- Productivity measures and quality frameworks that enhance business processes.
- TM cleanup and corpus preparation techniques for engine training (generic versus domain specific engines).
- MT Post Editing challenges.
- New business applications for MT: S2S, video, mobile, SEO, emergency response, disaster management, social media
- API challenges such as tag handling and/or reordering
- Open Standards for MT
- Overview and comparisons of open source MT tools and services
- Skills needed to support advances in AI approaches to machine translation.
Submission deadline: 11 December 2017
Notification of acceptance: 15 January 2018
Final “camera-ready” versions: 16 February 2018
What to submit
Please submit a 250 to 500-word abstract describing the topic of your presentation to the Commercial MT Users Chairs (email@example.com). Should you have any questions, use this same email address. Presentations will describe how MT services, as well as complementary systems, technologies, tools and processes address specific business challenges. Submissions should not contain commercial solicitations of specific tools. If you have original software that you would like to show, you may also consider submitting a proposal to the technology showcase.
Please indicate whether you intend to submit your presentation for publication in the AMTA 2018 Conference Proceedings. Publication in the Conference Proceedings is not a requirement, but we strongly encourage you to make your presentation available in the Proceedings so that other people can learn from your experiences. If you agree to have your presentation published in the Proceedings, you should format it according to the Research Track submission guidelines. However, slide decks are also acceptable. Only abstracts are required to be submitted by the initial submission date. Papers and slide decks will be accepted by the final camera-ready date for publication in the Proceedings.
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AMTA-2018 solicits original research papers that will advance the field of Machine Translation. In addition to regular contributions, we are also seeking extended abstracts, which can report in-progress work, or novel applications of technology to real application scenarios. Submissions must be unpublished, and in English.
We seek submissions across the entire spectrum of MT-related research activity, but put a particular focus on AMTA’s strength: the close interaction between researchers and practitioners who are looking to apply the latest MT technology to their tasks. Thus, we particularly encourage submissions that are oriented towards building robust and practical systems, including user-in-the-loop translation systems, adaptation to particular domains or usage scenarios, and utilization of available resources in production scenarios.
Submission deadline: Monday, 11 December 2017
Notification of acceptance: Monday, 15 January 2018
Final “camera-ready” versions: Wednesday, 14 February 2018
Full papers must not exceed 12 (twelve) pages plus 2 (two) pages for references, and must be formatted according to the AMTA 2018 style guide: PDF version / LaTeX version / MS Word version. These papers will be rigorously reviewed for novelty and impact, and published in the AMTA proceedings. They will be presented at the conference as either oral presentations or as posters.
We will also be accepting submissions of extended abstracts of no more than 6 (six) pages plus 2 (two) pages for references. These abstracts can be used to report in-progress or late-breaking research results, analyses of the effects of applying research technology to practical application scenarios, or descriptions of demos appearing at the technology showcase. Abstract submissions are further divided into two subcategories:
1. Original contributions, which will be included in the conference proceedings upon acceptance.
2. Non-archival submissions, which will not not appear in the proceedings, but will still be presented at the conference.
Both types of abstracts will be double-blind reviewed for informativeness, correctness and clarity. They will be presented at the conference as posters. Abstracts should be anonymized, and should put “This is a submission to the [original / non-archival] extended abstract track.” at the end of the abstract field in the START submission page (it does not need to be noted in the paper itself).
Submitted papers must be in PDF. To allow for blind reviewing, please do not include author names and affiliations within the paper, and avoid obvious self-references. Papers must be submitted to the START system (https://www.softconf.com/amta2018/papers/) by 11:59 pm PDT (GMT – 7 hours), Monday, 4 December 2017.
Topics of interest include but are not limited to:
- Advances in various MT paradigms: data-driven, rule-based, and hybrids;
- MT applications and embedding: translation/localization aids, speech-to-speech, speech-to-text, OCR, MT for communication (chats, blogs, social networks), multilingual applications, etc.
- Technologies for MT deployment: quality estimation and domain adaptation;
- MT in special settings: low resources, massive resources, high volume, low computing resources;
- Human factors in MT and user interfaces for MT;
- Linguistic resources for MT: dictionaries, terminology banks, corpora;
- MT evaluation techniques and evaluation results
- Empirical studies on translation data
Full papers and extended abstracts that will appear in the AMTA proceedings must represent new work that has not been previously published (pre-prints posted online on servers such as arXiv do not count as published papers, and thus are allowed to be submitted). It is the responsibility of the author(s) to inform the program chairs of any potential problem with respect to this requirement. Authors submitting a similar paper both to AMTA and another conference or workshop must inform the program chairs by email (firstname.lastname@example.org), specifying to which other conference or workshop they are submitting their work. If a paper is accepted at both AMTA and another conference, then to appear at AMTA it can either be presented at AMTA as a full paper and withdrawn from the other conference, or it can be withdrawn from the AMTA proceedings, but still presented at AMTA as a non-archival extended abstract.
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— Introduction to MT (Jay Marciano)
— ModernMT (Marcello Federico and Marco Trombetti)
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Introduction to MT October 28, Morning
This tutorial is for people who are beginning their journey with machine translation and want an overview of what it is, how it works, how it can be used, and whether it can fulfil their needs. No previous knowledge of machine translation is assumed, and all levels of skepticism are welcome. The focus will be on providing background knowledge that will help you get more out of the rest of the AMTA conference and make more informed decisions about how to use or invest in machine translation. Past participants have ranged from translation professionals who want to understand changes in their field to corporate executives who are evaluating technology strategies for their organizations. The main topics for discussion are common questions about MT (What is MT and how does it differ from other translation technologies? How well can machines really translate? What are the latest trends in MT research and development?), the quality of the translations it produces (Why is the output sometimes so bad? How can the quality be improved? Can translation quality be measured objectively?), and its application (What is MT good for? Have we reached the point of Star Trek’s universal translator? Will we? Can MT improve a translator’s efficiency? What are the implications of this technology for translators?). You will leave this tutorial with the tools you need to take part in an informed discussion of MT.
Presenter: Jay Marciano
Dependency-Based Statistical Machine Translation October 28, Afternoon
Syntax-based translation models learn translation patterns from recursive structures over sentences. Compared to phrase-based models (Koehn et al., 2003) they have a better capability of long-distance reordering and generalization, especially for MT between distant languages. Constituent structures have been widely used in statistical machine translation (SMT), but translation models built on constituent structures are computationally complex and inefficient when multi-level rules are taken into consideration. By contrast, dependency structures do not attract enough attention in SMT, although dependency grammar is regarded to be very helpful because it directly encodes semantic information and has the best inter-lingual phrasal cohesion properties (Fox, 2002).
In this tutorial, we will introduce representative work on dependency-based SMT, including:
- — Translation models based on segmentation:
o Dependency treelet models
o Dependency graph segmentation models
- — Translation models based on synchronous grammars:
o String-to-dependency models
o Dependency-to-string models
o Dependency-graph-to-string models
- — Dependency-based evaluation
- — Lab session with our open source tools
Presenters: Qun Liu and Liangyou Li
Computer Aided Translation: Advances and Challenges November 1, Morning
Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations. This tutorial will explain the main techniques for several aspects of computer aided translation:
– confidence measures
– interactive machine translation (interactive translation prediction)
– bilingual concordancers
– translation option display
– online adaptation
– eye tracking, logging, and cognitive user models
For each of these, the state of the art and open challenges are presented.
The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a “Home Edition” to be installed on a desktop machine. The target audience of this tutorial are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology.
Presenter: Philipp Koehn
Advances in Neural Machine Translation November 1, Afternoon
Neural machine translation has been introduced to the field of natural language processing and machine translation. Unlike existing approaches to machine translation, neural machine translation tackles the problem of translation by directly modelling the conditional probability of a translation given a source sentence without any assumption on factorization. Already in two years, neural machine translation has proven itself to be competitive against the existing translation approaches in many language pairs, which has excited many researchers in the field. In the first part of this tutorial, I will give an introduction to neural machine translation together with basics in connectionist natural language processing. This will be followed by describing new opportunities in machine translation that have become possible by introducing deep learning to machine translation. These opportunities include sub-word/character-level translation, multilingual translation and simultaneous machine translation.
Presenters: Rico Sennrich, Alexandra Birch, and Marcin Junczys-Dowmunt
ModernMT November 1, Afternoon
The ModernMT project aims at making a contribution to the evolution of machine translation. Our goal is to consolidate the current state of the art technology into a single easy-to-use product, evolving it and keeping it open to integrate the next greatest opportunities in machine intelligence, like deep learning.
In particular, we will introduce and demonstrate the ModernMT system architecture, whose distinguishing features make it particularly useful to be integrated in the Computer-Assisted Translation framework, namely its capability to (i) adapt in real time to the document to be translated and (ii) quickly learn from data provided by the users, such as translation memories and post-edited data. The MMT architecture builds on open source software (Moses, Lucene, etc.) and comes as a-ready-to-install application, that does not require any initial training phase and that enables scalability of data and users.
Our tutorial will cover the following aspects of the ModernMT project:
- — Introduction: main features and development roadmap
- — Development: software architecture and components
- — Field testing: ongoing testing activities with the industry and results
- — Integration: deployment and integration of ModernMT in CAT tools
- — Hands on session: participants will learn how to install, start, and use ModernMT
Presenters: Marcello Federico and Marco Trombetti
The call for workshop proposals is closed.
Contact: Roland Kuhn (email@example.com)
AMTA workshops are intended to provide the opportunity for MT-related communities of interest to spend time together advancing the state of thinking or the state of practice in their area of endeavor. We are particularly interested in submissions related to commercialization of MT and/or its use by professional translators. However, any themes connected to MT research, development, deployment, use, and evaluation are welcome. Topics for past workshops have included post-editing, treatment of Arabic-script languages, translation of patents and scientific literature, collaborative translation, lexical resources, and the impact of MT research on the translation industry.
– Proposal deadline: Thursday, June 1, 2016
– Notification of workshop acceptance: Ongoing
– Camera-ready deadline for workshops with proceedings: September 12, 2016
– Workshop days: Saturday, October 29 or Wednesday, November 2.
NOTE: It is possible that workshops scheduled for Nov. 2 may be organized jointly with EMNLP.
How to submit:
Workshop proposals must be submitted to firstname.lastname@example.org. They should
– Workshop title
– Dates for important milestones (call for papers, recruitment of speakers, etc.)
– Description of the workshop content, technical requirements
– Expected number of participants
– Whether this is an ongoing or new workshop, and
– A signed copy of the AMTA2016_Workshop_Proposal_Form
Neural Machine Translation technology is progressing at a very rapid pace. In the last few years, the research community has proposed several different architectures with various levels of complexity. However, even complex Neural Networks are really built from simple building blocks; and their functioning is governed by relatively simple rules. In this tutorial, we aim […]
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Tutorial Presenters: Nicola Ueffing (eBay MT Science), Pete Smith (University of Texas Arlington) and Silvio Picinini (eBay Localization) Target audience: any person involved with the deployment of MT that is interested in the quality of the corpora data that will be at the foundation of the MT deployment. Roles include managers, linguists, engineers or scientists. […]
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Boston, Massachusetts, March 21, 2018 The goal of quality estimation is to evaluate a translation system’s quality without access to reference translations (Blatz et al., 2004; Specia et al., 2013). This has many potential usages: informing an end user about the reliability of translated content; deciding if a translation is ready for publishing or if […]
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https://sites.google.com/view/loresmt/ Statistical and neural machine translation (SMT/NMT) methods have been successfully used to build MT systems in many popular languages in the last two decades with significant improvements on the quality of automatic translation. However, these methods still rely upon a few natural language processing (NLP) tools to help pre-process human generated texts in the […]
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AMTA will be hosting a one-day Technology Forum and is looking for demonstrations of Commerical products Government systems Open source software Research prototypes Other cool new software In the following areas: Machine translation Computer Assisted Translation, including hybrid CAT/MT systems Any technology that supports translation (e.g., Language ID) There is no exhibit fee. Exhibiters coming […]
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Contact: Steve Richardson (email@example.com) AMTA 2018 is seeking proposals for tutorials on all topics related to MT. We are interested in tutorials presented by experts from the following categories: – MT Researchers and Developers – Commercial MT Users – Government MT Users – Translators using MT Any themes connected to MT research, development, deployment, use, […]
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