Noami Yamashita and Toru Ishida (Kyoto) Computer Supported Cooperative Work (CSCW 2006). [pdf]
Abstract: Even though multilingual communities that use machine translation to overcome language barriers are increasing, we still lack a complete understanding of how machine translation affects communication. In this study, eight pairs from three different language communities–China, Korea, and Japan–worked on referential tasks in their shared second language (English) and in their native languages using a machine translation embedded chat system. Drawing upon prior research, we predicted differences in conversational efficiency and content, and in the shortening of referring expressions over trials. Quantitative results combined with interview data show that lexical entrainment was disrupted in machine translation-mediated communication because echoing is disrupted by asymmetries in machine translations. In addition, the process of shortening referring expressions is also disrupted because the translations do not translate the same terms consistently throughout the conversation. To support natural referring behavior in machine translation-mediated communication, we need to resolve asymmetries and inconsistencies caused by machine translations.

Task for experiments: order figures through a chat interface, via a third language (English) and with own lanuage+ MT.
The process of agreeing on a perspective on a referent is known as lexical entrainment [4, 11].

Although machine translation liberates members from language barriers, it also poses hurdles for establishing mutual understanding. As one might expect, translation errors are the main source of inaccuracies that complicate mutual understanding [25]. Climent found that typographical errors are also a big source of translation errors that hinder mutual understanding [7]. Yamashita discovered that members tend to misunderstand translated messages and proposed a method to automatically detect misunderstandings [30].

In machine translation-mediated communication, shortened referring expressions are not necessarily translated correctly; even when referring expressions overlap considerably, machine translation may generate something totally different based on very small changes. Because abbreviation is problematic for machine translation, we expect that participants will identify a figure using identical referring expressions throughout the conversation.

… translations between two different languages are not transitive: translation from language A to B and back to A does not yield the original expression. The intransitive nature of machine translations results from its development process; translation from language A to B is built independently of translation from language B to A. In such conversations, the addressee cannot echo the speaker’s expression as a way of accepting it, illustrating that they are referring to the same thing.

We also found that in their second trial, speakers using machine translation preferred to narrow expressions rather than simplify them. …We infer that “narrowing” is observed more frequently in machine translation-mediated communication because distinctive terms such as “kimono” have few alternatives in translation, and thus, participants feel safe using them to match the figures.

Moreover, participants avoided focusing on the incomprehensible part of messages to discover what was wrong. Since translations are not transitive, it appears that they cannot efficiently solve the problem. Speakers have little choice but to offer more information and proceed with the task.

Consistent with quantitative results, speakers tended to describe the figures more frequently in machine translation than in English.

It seems that participants can minimize mutual effort in collaboration by offering more and more information until their partner confirms understanding.

Since such an unwieldy conversational style would not be useful in general conversation, there is a need to support natural referential behavior in machine translation-mediated communication. For example, support that creates correspondences among references (or keywords) between the two languages may help. Also, support that creates correspondences among referring expressions before and after shortening may help.


… when I took a look at Ed Bice’s slides for the AMTA Social Impact of MT Panel. Ed Bice is the founder of Meadan (ebice @, among many other things (his Pop web page).

hybrid distributed natural language translation (hdnlt) ‘web 2.0’ approach
• Language translation as a distributed service
• People/machines collaborate to provide service
• Volunteer translators as a social network
• Harness collective intelligence – value arises from small, shared
• Reputation driven – translator reputations adjusted by feedback
and performance
• Abstractions ease adding devices and services

By Erick Schonfeld, Om Malik, and Michael V. Copeland


Incumbent To Watch: Yahoo!
Hoping to dominate social media, it’s gobbling up promising startups (, Flickr, Webjay) and experimenting with social search (My Web 2.0) that ranks results based on shared bookmarks and tags.


Incumbent To Watch: Google
Already the ultimate Web filter through general search as well as blog, news, shopping, and now video search, it’s encouraging mashups of Google Maps and search results, and offers a free RSS reader.


For nearly a century, the phone, and voice as we know it, has existed largely in the confines of a thin copper wire. But now service providers can convert voice calls into tiny Internet packets and let them loose on fast connections, thus mimicking the traditional voice experience without spending hundreds of millions on infrastructure. All you need are powerful–but cheap–computers running specialized software. The Next Net will be the new phone, creating fertile ground for new businesses.

Incumbent To Watch: eBay (Skype)
The pioneer in the field and still the front-runner, Skype brings together free calling, IM, and video calling over the Web; eBay will use it to create deeper connections between buyers and sellers. [And I’d say Google Talk is following closely…]


It’s been a long time — all the way back to the dawn of desktop computing in the early 1980s — since software coders have had as much fun as they’re having right now. But today, browser-based applications are where the action is. A killer app no longer requires hundreds of drones slaving away on millions of lines of code. Three or four engineers and a steady supply of Red Bull is all it takes to rapidly turn a midnight brainstorm into a website so hot it melts the servers. What has changed is the way today’s Web-based apps can run almost as seamlessly as programs used on the desktop, with embedded audio, video, and drag-and-drop ease of use. Company: 37Signals (Chicago)
What it is: Online project management
Next Net bona fides: Its Basecamp app, elegant and inexpensive, enables the creation, sharing, and tracking of to-do lists, files, performance milestones, and other key project metrics; related app Backpack, recently released, is a powerful online organizer for individuals.
Company: Writely (Portola Valley, CA)
What it is: Online word processing
Next Net bona fides: It enables online creation of documents, opens them to collaboration by anyone anywhere, and simplifies publishing the end result on a website as a blog entry.


A growing number of companies are either offering themselves as Web-based platforms on which other software and businesses can be built or developing basic tools that make some of the defining hallmarks of the Next Net possible.

Incumbent To Watch: Amazon
It’s becoming a major Web platform by opening up its software protocols and encouraging anyone to use its catalog and other data; its Alexa Web crawler, which indexes the Net, can be used as the basis for other search engines, and its Mechanical Turk site solicits humans across cyberspace to do things that computers still can’t do well, such as identify images or transcribe podcasts.

Mellebeek, Bart; Owczarzak, Karolina; Van Genabith, Josef & Way, Andy. (2006). AMTA, Boston, MA.

Original paper on TransBooster project is: B. Mellebeek, A. Khasin, J. Van Genabith, A. Way. 2005. TransBooster: Boosting the Performance of Wide-Coverage Machine Translation Systems. In Proceedings of the 10th Annual Conference of the European Association for Machine Translation. pp. 189-197, Budapest, Hungary.

Abstract: In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine
Translation (MEMT) techniques, we do ot rely on word alignments of output hypotheses, but prepare the input sentence or multi-engine processing. We do this by using a recursive decomposition algorithm hat produces simple chunks as input to the MT engines. A consensus translation is produced by combining the best chunk translations, selected through majority voting, a trigram language model score and a confidence score assigned to each MT engine. We report statistically significant relative improvements
of up to 9% BLEU score in experiments (English->Spanish) carried out on an 800-
sentence test set extracted from the Penn-II Treebank.

Summary: They describe an algorithm for splitting input sentences into syntactically meaningful chunks (according to a parser/human) and simplifying the arguments of a pivot (head of the chunk) to facilitate the machine translation process of the simplified chunks in (dynamically simplified) context.

My Notes: this work shows that splitting up long input sentences into shorter one, can actually lead to improvement of MT output in terms of BLEU. Therefore having a game with a purpose trying to do this using humans, becomes less relevant.

In contrast to previous MEMT approaches, the technique we present does not rely on word alignments of target language sentences, but is based on recursive chunking algorithm that produces simple constituents as input to the MT engines. The outputs of these syntactically meaningful chunks are compared to each other and the highest ranked translations are used to compose the output sentence. Our approach, therefore, prepares the input sentence for multi-engine processing on the input side. It draws its strength from the simple fact that short input strings result in better translations than longer ones.

The decomposition into chunks, the tracking of the output chunks in target and the final composition of the output are based on the TransBooster architecture presented in (Mellebeek et al., 2005) [EAMTA, Budapest].

Our approach presupposes the existence of some sort of syntactic analysis of the input sentence. In a first step, the input sentence is decomposed into a number of syntactically meaningful chunks as in (1).
(1) [ARG_1] [ADJ_1]. . . [ARG_L] [ADJ_l] pivot [ARG_L+1] [ADJ_l+1]. . . [ARG_L+R] [ADJ_l+r]
where pivot = the nucleus of the sentence, ARG = argument, ADJ = adjunct, {l,r} = number of ADJs to left/right of pivot, and {L,R} = number of ARGs to left/right of pivot.
In order to determine the pivot, we compute the head of the local tree by adapting the headlexicalised rammar annotation scheme of (Magerman, 1995). In certain cases, we derive a ‘complex pivot’ consisting of the head terminal together with some of its neighbours, e.g. phrasal verbs or strings of auxiliaries. The procedure used for argument/
adjunct identification is an adapted version of Hockenmaier’s algorithm for CCG (Hockenmaier, 2003).

In a next step, we replace the arguments by similar but simpler strings, which we call ‘Substitution Variables’. The purpose of Substitution Variables is: (i) to help to reduce the complexity of the original arguments, which often leads to an improved translation of the pivot; (ii) to help keep track of the location of the translation of the arguments in target.
In choosing an optimal Substitution Variable for a constituent, there exists a trade-off between accuracy and retrievability. ‘Static’ or previously defined Substitution Variables (e.g. ‘cars’ to replace the NP ‘fast and confidential deals’ as explained in section 3.5) are easy to track in target, since their translation by a specific MT engine is known in advance,
but they might distort the translation of the pivot because of syntactic/semantic differences with the original constituent. ‘Dynamic’ Substitution Variables comprise the real heads of the constituent (e.g. ‘deals’ to replace the NP ‘fast and confidential deals’
as outlined in section 3.5) guarantee a maximum similarity, but are more difficult to track in target.
Our algorithm employs Dynamic Substitution Variables first and automatically backs off to Static Substitution Variables if problems occur. By replacing the arguments by their Substitution Variables and leaving out the adjuncts in (1), we obtain the skeleton
in (2)

(2) [VARG_1 ] . . . [VARG_L] pivot [VARG_L+1] . . . [VARG_L+R]
where VARGi is the simpler string substituting ARGi
By matching the previously established translations of the Substitution Variables VARGi (1 <= i <= L + R) in the translation of the skeleton in (2), we are able to (i) extract the translation of the pivot and (ii) track the location of the translated arguments in target. The result of this second step on the worked example is shown in (6). Adjuncts are located in target by using a similar strategy in which adjunct Substitution Variables are
added to the skeleton in (2).

Since translating individual chunks out of context is likely to produce a deficient output or lead to boundary friction, we need to ensure that each chunk is translated in a simple context that mimics the original.
As in the case of the Substitution Variables, this context can be static (a previously established template, the translation of which is known in advance) or dynamic (a simpler version of the original context).
Our approach is based on the idea that by reducing the complexity of the original context, the analysis modules of the MT engines are more likely to produce a better translation of the input chunk Ci than if it were left intact in the original sentence, which contains more syntactic and semantic ambiguities.
In other words, we try to improve on the translation C_ji of chunk C_i by MT engine j through input simplification. (cf. section 3.5 for more details)
After obtaining the translations of all input chunks by all MT engines (C_i1 – C_iN ), all that remains to be done is to select the best output translation C_i_best for each chunk C_i and derive the output by composing all C_i_best . This is possible since we have kept track of the position of each C_ij by the Substitution Variables.

Nicole Lazzaro, President (Abstract March 8, 2004). Player Experience Research and Design for Mass Market Interactive Entertainment.

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