58,158 research outputs found
Taking statistical machine translation to the student translator
Despite the growth of statistical machine translation (SMT) research and development in recent years, it remains somewhat out of reach for the translation community where programming expertise and knowledge of statistics tend not to be commonplace. While the concept of SMT is relatively straightforward, its implementation in functioning systems remains difficult for most, regardless of expertise. More recently, however, developments such as SmartMATE have emerged which aim to assist users in creating their own customized SMT systems and thus reduce the learning curve associated with SMT. In addition to commercial uses, translator training stands to benefit from such increased levels of inclusion and access to state-of-the-art approaches to MT. In this paper we draw on experience in developing and evaluating a new syllabus in SMT for a cohort of post-graduate student translators: we identify several issues encountered in the introduction of student translators to SMT, and report on data derived from repeated measures questionnaires that aim to capture data on studentsâ self-efficacy in the use of SMT. Overall, results show that participants report significant increases in their levels of confidence and knowledge of MT in general, and of SMT in particular. Additional benefits â such as increased technical competence and confidence â and future refinements are also discussed
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Task-oriented dialog systems are becoming pervasive, and many companies
heavily rely on them to complement human agents for customer service in call
centers. With globalization, the need for providing cross-lingual customer
support becomes more urgent than ever. However, cross-lingual support poses
great challenges---it requires a large amount of additional annotated data from
native speakers. In order to bypass the expensive human annotation and achieve
the first step towards the ultimate goal of building a universal dialog system,
we set out to build a cross-lingual state tracking framework. Specifically, we
assume that there exists a source language with dialog belief tracking
annotations while the target languages have no annotated dialog data of any
form. Then, we pre-train a state tracker for the source language as a teacher,
which is able to exploit easy-to-access parallel data. We then distill and
transfer its own knowledge to the student state tracker in target languages. We
specifically discuss two types of common parallel resources: bilingual corpus
and bilingual dictionary, and design different transfer learning strategies
accordingly. Experimentally, we successfully use English state tracker as the
teacher to transfer its knowledge to both Italian and German trackers and
achieve promising results.Comment: 13 pages, 5 figures, 3 tables, accepted to EMNLP 2018 conferenc
Advanced Speech Communication System for Deaf People
This paper describes the development of an Advanced Speech Communication System for Deaf People and its field evaluation in a real application domain: the renewal of Driverâs License. The system is composed of two modules. The first one is a Spanish into Spanish Sign Language (LSE: Lengua de Signos Española) translation module made up of a speech recognizer, a natural language translator (for converting a word sequence into a sequence of signs), and a 3D avatar animation module (for playing back the signs). The second module is a Spoken Spanish generator from sign writing composed of a visual interface (for specifying a sequence of signs), a language translator (for generating the sequence of words in Spanish), and finally, a text to speech converter. For language translation, the system integrates three technologies: an example based strategy, a rule based translation method and a statistical translator. This paper also includes a detailed description of the evaluation carried out in the Local Traffic Office in the city of Toledo (Spain) involving real government employees and deaf people. This evaluation includes objective measurements from the system and subjective information from questionnaire
MAT: A Multimodal Attentive Translator for Image Captioning
In this work we formulate the problem of image captioning as a multimodal
translation task. Analogous to machine translation, we present a
sequence-to-sequence recurrent neural networks (RNN) model for image caption
generation. Different from most existing work where the whole image is
represented by convolutional neural network (CNN) feature, we propose to
represent the input image as a sequence of detected objects which feeds as the
source sequence of the RNN model. In this way, the sequential representation of
an image can be naturally translated to a sequence of words, as the target
sequence of the RNN model. To represent the image in a sequential way, we
extract the objects features in the image and arrange them in a order using
convolutional neural networks. To further leverage the visual information from
the encoded objects, a sequential attention layer is introduced to selectively
attend to the objects that are related to generate corresponding words in the
sentences. Extensive experiments are conducted to validate the proposed
approach on popular benchmark dataset, i.e., MS COCO, and the proposed model
surpasses the state-of-the-art methods in all metrics following the dataset
splits of previous work. The proposed approach is also evaluated by the
evaluation server of MS COCO captioning challenge, and achieves very
competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40)
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