This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.

The need to modify an application so that it can support different languages and cultural settings can appear once the application is finished and even in the market. This may introduce serious time delays and an increase in costs. We solve this problem for web-based software through JITL, a post-hoc method to automatically internationalize websites and web-based applications, without having to modify the source code. With JITL, users can pull resource strings out of an arbitrary website and perform on-demand localization tasks. Based on this novel capability, JITL enables a complete infrastructure for collecting, storing, sharing, and delivering website translations, which invokes a number of exciting scenarios. Our studies show that JITL leads to significant savings in terms of user effort and, in consequence, money. With JITL, now it is possible to localize what is needed, when it is needed.

Translating the text in an interface is a challenging task. Besides the jargon and technical terms, many of the strings are often very short, such as those shown in buttons and pull-down menus. Then, as a result of the lack of visual context in the traditional localization process, an important ambiguity problem arises. We study three approaches to solve this problem: using plain gettext (baseline condition), using gettext plus being able to operate the UI, and translating the UI in-place. We found that translators are substantially faster with plain gettext but commit a significantly higher number of errors in comparison to the other approaches. Unexpectedly, the mixed condition was slower and more error-prone than in-place translation. The latter was found to be comparable to plain gettext in terms of time, although some strings passed unnoticed as the UI was operated. Based on our results, we arrive at a set of recommendations to augment localization tools to improve translator's productivity.

CASMACAT is a modular, web-based translation workbench that offers advanced functionalities for computer-aided translation and the scientific study of human translation: automatic interaction with machine translation (MT) engines and translation memories (TM) to obtain raw translations or close TM matchesn for conventional post-editing; interactive translation prediction based on an MT engine’s search graph, detailed recording and replay of edit actions and translator’s gaze (the latter via eye-tracking), and the support of e-pen as an alternative input device. The system is open source sofware and interfaces with multiple MT systems.

Automatic localization of cultural resources and UIs is crucial for the survival of minority languages, for which there are insufficient parallel corpora (or no corpus at all) to build machine translation systems. This paper proposes a new way to compensate for such resource-scarce languages, based on the fact that most languages share a common vocabulary. Concretely, our approach leverages a family of languages closely related to the speaker's native language to construct translations in a coherent mix of these languages. Experimental results indicate that these translations can be easily understood, being also a useful aid for users who are not proficient in foreign languages. Therefore this work significantly contributes to HCI in two ways: it establishes a language that can improve how applications communicate to their users, and it reports insights on the user acceptance towards the method.

Interactive machine translation (IMT) is an increasingly popular paradigm for semi-automated machine translation, where a human expert is integrated into the core of an automatic machine translation system. The human expert interacts with the IMT system by partially correcting the errors of the system's output. Then, the system proposes a new solution. This process is repeated until the output meets the desired quality. In this scenario, the interaction is typically performed using the keyboard and the mouse. However, speech is also a very interesting input modality since the user does not need to abandon the keyboard to interact with it.

In this paper we present a new way of translating documents by using a Web-based system. An interactive approach is proposed as an alternative to post-editing the output of a machine translation system. In this approach, the user's feedback is used to validate or to correct parts of the system output that allow the generation of improved versions of the rest of the output.

Traditionally, Natural Language Processing (NLP) technologies have mainly focused on full automation. However, full automation often proves unnatural in many applications, where technology is expected to assist rather than replace the human agents.