HCI for the deaf community: developing human-like avatars for sign language synthesis

With ever increasing computing power and advances in 3D animation technologies it is no surprise that 3D avatars for sign language (SL) generation are advancing too. Traditionally these avatars have been driven by somewhat expensive and inflexible motion capture technologies and perhaps this is the reason avatars do not feature in all but a few user interfaces (UIs). SL synthesis is a competing technology that is less costly, more versatile and may prove to be the answer to the current lack of access for the Deaf in HCI. This paper outlines the current state of the art in SL synthesis for HCI and how we propose to advance this by improving avatar quality and realism with a view to ameliorating communication and computer interaction for the Deaf community as part of a wider localisation project

Mitigating problems in analogy-based EBMT with SMT and vice versa: a case study with named entity transliteration

Five years ago, a number of papers reported an experimental implementation of an Example Based Machine Translation (EBMT) system using proportional analogy. This approach, a type of analogical learning, was attractive because of its simplicity; and the paper reported considerable success with the method using various language pairs. In this paper, we describe our attempt to use this approach for tackling English–Hindi Named Entity (NE) Transliteration. We have implemented our own EBMT system using proportional analogy and have found that the analogy-based system on its own has low precision but a high recall due to the fact that a large number of names are untransliterated with the approach. However, mitigating problems in analogy-based EBMT with SMT and vice-versa have shown considerable improvement over the individual approach

Statistically motivated example-based machine translation using translation memory

In this paper we present a novel way of integrating Translation Memory into an Example-based Machine translation System (EBMT) to deal with the issue of low resources. We have used a dialogue of 380 sentences as the example-base for our system. The translation units in the Translation Memories are automatically extracted based on the aligned phrases (words) of a statistical machine translation (SMT) system. We attempt to use the approach to improve translation from English to Bangla as many statistical machine translation systems have difficulty with such small amounts of training data. We have found the approach shows improvement over a baseline SMT system

Building a sign language corpus for use in machine translation

In recent years data-driven methods of machine translation (MT) have overtaken rule-based approaches as the predominant means of automatically translating between languages. A pre-requisite for such an approach is a parallel corpus of the source and target languages. Technological developments in sign language (SL) capturing, analysis and processing tools now mean that SL corpora are becoming increasingly available. With transcription and language analysis tools being mainly designed and used for linguistic purposes, we describe the process of creating a multimedia parallel corpus specifically for the purposes of English to Irish Sign Language (ISL) MT. As part of our larger project on localisation, our research is focussed on developing assistive technology for patients with limited English in the domain of healthcare. Focussing on the first point of contact a patient has with a GP’s office, the medical secretary, we sought to develop a corpus from the dialogue between the two parties when scheduling an appointment. Throughout the development process we have created one parallel corpus in six different modalities from this initial dialogue. In this paper we discuss the multi-stage process of the development of this parallel corpus as individual and interdependent entities, both for our own MT purposes and their usefulness in the wider MT and SL research domains

A review of EBMT using proportional analogies

Some years ago a number of papers reported an experimental implementation of Example Based Machine Translation (EBMT) using Proportional Analogy. This approach, a type of analogical learning, was attractive because of its simplicity; and the papers reported considerable success with the method. This paper reviews what we believe to be the totality of research reported using this method, as an introduction to our own experiments in this framework, reported in a companion paper. We report first some lack of clarity in the previously published work, and then report our findings that the purity of the proportional analogy approach imposes huge run-time complexity for the EBMT task even when heuristics as hinted at in the original literature are applied to reduce the amount of computation