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Low Resources Machine TranslationCarl, Michael; Maite, Melero; Badia, Toni; Vandeghinste, Vincent; Dirix, Peter; Schuurman, Ineke; Markantonatou, Stella; Sofianopoulos, Sokratis; Vassiliou, Marina; Yannoutsou, Olga (, 2008)[More information][Less information]
Abstract: METIS-II was a EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use ‘basic’ linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their ‘home’ languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions. URI: http://hdl.handle.net/10398/8037 Files in this item: 1
METIS-II.pdf (503.5Kb) -
Carl, Michael (, 2008)[More information][Less information]
Abstract: One of the aims of the Eye-to-IT project is to investigate the possibility of using eye-tracking devices for detecting situations of targeted help for human translators. A prerequisite for automated assistance in human translation is the understanding and modelling of reading behaviour, the ability to follow human eye movements and to map gaze sample points — the output of eyetracking devices — onto words and symbols fixated. Within the Eye-to-IT project we currently use a so-called “Gaze-to- Word Mapping” (GWM) device (ˇSpakov 2008) that first computes possible fixations from sequences of gaze sample coordinates and then maps the fixations on the words which are likely to be fixated. This paper suggests an alternative framework of a probabilistic gaze mapping model for reading, in which fixations on textual objects are directly computed from the gaze sample points. The framework integrates various knowledge sources with the aim to compute the most likely fixations on words and symbols on the basis of the available data. URI: http://hdl.handle.net/10398/8043 Files in this item: 1
CLS.pdf (186.2Kb) -
Carl, Michael; Doherty, Stephen; O’Brien, Sharon (Preprint, 2010)[More information][Less information]
Abstract: Eye tracking has been used successfully as a technique for measuring cognitive load in reading, psycholinguistics, writing, language acquisition etc for some time now. Its application as a technique for automatically measuring the reading ease of MT output has not yet, to our knowledge, been tested. We report here on a preliminary study testing the use and validity of an eye tracking methodology as a means of semi- and/or automatically evaluating machine translation output. 50 French machine translated sentences, 25 rated as excellent and 25 rated as poor in an earlier human evaluation, were selected. 10 native speakers of French were instructed to read the MT sentences for comprehensibility. Their eye gaze data were recorded non-invasively using a Tobii 1750 eye tracker. The average gaze time and fixation count were found to be higher for the “bad” sentences, while average fixation duration and pupil dilations were not found to be substantially different between output rated as good or bad. Comparisons between BLEU scores and eye gaze data were also made and found to correlate well with gaze time and fixation count, and to a lesser extent with pupil dilation and fixation duration. We conclude that the eye tracking data, in particular gaze time and fixation count, correlate reasonably well with human evaluation of MT output but fixation duration and pupil dilation may be less reliable indicators of reading difficulty for MT output. We also conclude that eye tracking has promise as an automatic MT Evaluation technique. URI: http://hdl.handle.net/10398/8045 Files in this item: 1
SubmissionforMT_dohertyobriencarl.pdf (226.2Kb) -
Quantifying alignment units with keystroke dataCarl, Michael (, 2009)[More information][Less information]
Abstract: The paper discusses a method to triangulate process and product data. We suggest converting Translog data into a relational format which contains both process and product data. We outline how this representation allows us to retrieve and correlate the various dimensions of the data more easily. The concept of Alignment Unit (AU) is introduced and contrasted with that of Translation Unit (TU). While AUs refer to translation equivalences in the source and target texts of the product data, TUs refer to cognitive entities that can be observed in the process data. With an (almost) exhaustive fragmentation of the source and target texts into AUs, we are able to distribute and allocate the entire set of keystroke data to appropriate AUs. Using the properties of the keystroke data, AUs are quantified in a novel way which enables us to visualise and investigate the structure of translation production on a fine-grained scale. URI: http://hdl.handle.net/10398/8040 Files in this item: 1
keystrokes.pdf (940.0Kb)
