25,716 research outputs found
Lost in translation: the problems of using mainstream MT evaluation metrics for sign language translation
In this paper we consider the problems of applying corpus-based techniques to minority languages that are neither politically recognised nor have a formally accepted writing system, namely sign languages. We discuss the adoption of an annotated form of sign language data as a suitable corpus for the development of a data-driven machine translation (MT) system, and deal with issues that arise from its use. Useful software tools that facilitate easy annotation of video data are also discussed. Furthermore, we address the problems of using traditional MT evaluation metrics for sign language translation. Based on the candidate translations produced from our example-based machine translation system, we discuss why standard metrics fall short of providing an accurate evaluation and suggest more suitable evaluation methods
Assistive translation technology for deaf people: translating into and animating Irish sign language
Machine Translation (MT) for sign languages (SLs) can facilitate communication between Deaf and hearing people by translating information into the native and preferred language of the individuals. In this paper, we discuss automatic translation from English to Irish SL (ISL) in the domain of airport information. We describe our data collection processes and the architecture of the MaTrEx system used for our translation work. This is followed by an outline of the additional animation phase that transforms the translated output into animated ISL. Through a set of experiments, evaluated both automatically and
manually, we show that MT has the potential to assist Deaf people by providing information in their first language
Improving Context Modelling in Multimodal Dialogue Generation
In this work, we investigate the task of textual response generation in a
multimodal task-oriented dialogue system. Our work is based on the recently
released Multimodal Dialogue (MMD) dataset (Saha et al., 2017) in the fashion
domain. We introduce a multimodal extension to the Hierarchical Recurrent
Encoder-Decoder (HRED) model and show that this extension outperforms strong
baselines in terms of text-based similarity metrics. We also showcase the
shortcomings of current vision and language models by performing an error
analysis on our system's output
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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