This paper surveys the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in Canada in preserving their languages and extending their use. The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central. Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen’kéha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for recordings of speech in Indigenous languages (and other languages), software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.

In this paper, we describe WeBiText (www.webitext.ca) and how it is being used. WeBiText is a concordancer that allows translators to search in large, high-quality multilingual web sites, in order to find solutions to translation problems. After a quick overview of the system, we present results from an analysis of its logs, which provides a picture of how the tool is being used and how well it performs. We show that it is mostly used to find solutions for short, two or three word translation problems. The system produces at least one hit for 58% of the queries, and hits from at least five different web pages in 41% of cases. We show that 36% of the queries correspond to specialized language problems, which is much higher than what was previously reported for a similar concordancer based on the Canadian Hansard (TransSearch). We also provide a back of the envelope calculation of the current economic impact of the tool, which we estimate at $1 million per year, and growing rapidly.

This paper is about Translation Dictation with ASR, that is, the use of Automatic Speech Recognition (ASR) by human translators, in order to dictate translations. We are particularly interested in the productivity gains that this could provide over conventional keyboard input, and ways in which such gains might be increased through a combination of ASR and Statistical Machine Translation (SMT). In this hybrid technology, the source language text is presented to both the human translator and a SMT system. The latter produces N-best translations hypotheses, which are then used to fine tune the ASR language model and vocabulary towards utterances which are probable translations of source text sentences. We conducted an ergonomic experiment with eight professional translators dictating into French, using a top of the line off-the-shelf ASR system (Dragon NatuallySpeaking 8). We found that the ASR system had an average Word Error Rate (WER) of 11.7 percent, and that translation using this system did not provide statistically significant productivity increases over keyboard input, when following the manufacturer recommended procedure for error correction. However, we found indications that, even in its current imperfect state, French ASR might be beneficial to translators who are already used to dictation (either with ASR or a dictaphone), but more focused experiments are needed to confirm this. We also found that dictation using an ASR with WER of 4 percent or less would have resulted in statistically significant (p less than 0.6) productivity gains in the order of 25.1 percent to 44.9 percent Translated Words Per Minute. We also evaluated the extent to which the limited manufacturer provided Domain Adaptation features could be used to positively bias the ASR using SMT hypotheses. We found that the relative gains in WER were much lower than has been reported in the literature for tighter integration of SMT with ASR, pointing the advantages of tight integration approaches and the need for more research in that area.

Machine Translation (MT) is rapidly progressing towards quality levels that might make it appropriate for broad user populations in a range of scenarios, including gisting and post-editing in unconstrained domains. For this to happen, the field may however need to switch gear and move away from its current technology driven paradigm to a more user-centered approach. In this paper, we discuss how ethnographic techniques like Contextual Inquiry could help in that respect, by providing researchers and developers with rich information about the world and needs of potential end-users. We discuss how data from Contextual Inquiries with professional translators was used to concretely and positively influence several research and development projects in the area of Computer Assisted Translation technology. These inquiries had many benefits, including: (i) grounding developers and researchers in the world of their end-users, (ii) generating new technology ideas, (iii) selecting between competing development project ideas, (iv) finding how to alleviate friction for important ideas that go against the grain of current user practices, (v) evaluating existing or experimental technologies, (vi) helping with micro level design decision, (vii) building credibility with translators, and (viii) fostering multidisciplinary discussion between researchers.

This paper presents an algorithm for correcting language errors typical of second-language learners. We focus on preposition errors, which are very common among second-language learners but are not addressed well by current commercial grammar correctors and editing aids. The algorithm takes as input a sentence containing a preposition error (and possibly other errors as well), and outputs the correct preposition for that particular sentence context. We use a two-phase hybrid rule-based and statistical approach. In the first phase, rule-based processing is used to generate a short expression that captures the context of use of the preposition in the input sentence. In the second phase, Web searches are used to evaluate the frequency of this expression, when alternative prepositions are used instead of the original one. We tested this algorithm on a corpus of 133 French sentences written by intermediate second-language learners, and found that it could address 69.9% of those cases. In contrast, we found that the best French grammar and spell checker currently on the market, Antidote, addressed only 3% of those cases. We also showed that performance degrades gracefully when using a corpus of frequent n-grams to evaluate frequencies.