eprintid: 25488
rev_number: 13
eprint_status: archive
userid: 4043
dir: disk0/00/02/54/88
datestamp: 2018-10-31 12:40:33
lastmod: 2018-12-19 10:36:37
status_changed: 2018-10-31 12:40:33
type: doctoralThesis
metadata_visibility: show
creators_name: Simianer, Patrick
title: Preference Learning for Machine Translation
subjects: ddc-004
divisions: i-90500
adv_faculty: af-09
cterms_swd: machine translation
cterms_swd: discriminative training
cterms_swd: computer-aided translation
abstract: 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.
date: 2018
id_scheme: DOI
id_number: 10.11588/heidok.00025488
ppn_swb: 1658548213
own_urn: urn:nbn:de:bsz:16-heidok-254882
date_accepted: 2018-04-23
advisor: HASH(0x55fc36c4fb00)
language: eng
bibsort: SIMIANERPAPREFERENCE2018
full_text_status: public
citation:   Simianer, Patrick  (2018) Preference Learning for Machine Translation.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/25488/1/diss-print.pdfa.pdf