3 research outputs found
Towards an Automatic Dictation System for Translators: the TransTalk Project
Professional translators often dictate their translations orally and have
them typed afterwards. The TransTalk project aims at automating the second part
of this process. Its originality as a dictation system lies in the fact that
both the acoustic signal produced by the translator and the source text under
translation are made available to the system. Probable translations of the
source text can be predicted and these predictions used to help the speech
recognition system in its lexical choices. We present the results of the first
prototype, which show a marked improvement in the performance of the speech
recognition task when translation predictions are taken into account.Comment: Published in proceedings of the International Conference on Spoken
Language Processing (ICSLP) 94. 4 pages, uuencoded compressed latex source
with 4 postscript figure
Preference Learning for Machine Translation
Automatic translation of natural language is still (as of 2017) a long-standing but unmet promise. While advancing at a fast rate, the underlying methods are still far from actually being able to reliably capture syntax or semantics of arbitrary utterances of natural language, way off transporting the encoded meaning into a second language. However, it is possible to build useful translating machines when the target domain is well known and the machine is able to learn and adapt efficiently and promptly from new inputs. This is possible thanks to efficient and effective machine learning methods which can be applied to automatic translation.
In this work we present and evaluate methods for three distinct scenarios:
a) We develop algorithms that can learn from very large amounts of data by exploiting pairwise preferences defined over competing translations, which can be used to make a machine translation system robust to arbitrary texts from varied sources, but also enable it to learn effectively to adapt to new domains of data;
b) We describe a method that is able to efficiently learn external models which adhere to fine-grained preferences that are extracted from a constricted selection of translated material, e.g. for adapting to users or groups of users in a computer-aided translation scenario;
c) We develop methods for two machine translation paradigms, neural- and traditional statistical machine translation, to directly adapt to user-defined preferences in an interactive post-editing scenario, learning precisely adapted machine translation systems.
In all of these settings, we show that machine translation can be made significantly more useful by careful optimization via preference learning