15,586 research outputs found
Target-Side Context for Discriminative Models in Statistical Machine Translation
Discriminative translation models utilizing source context have been shown to
help statistical machine translation performance. We propose a novel extension
of this work using target context information. Surprisingly, we show that this
model can be efficiently integrated directly in the decoding process. Our
approach scales to large training data sizes and results in consistent
improvements in translation quality on four language pairs. We also provide an
analysis comparing the strengths of the baseline source-context model with our
extended source-context and target-context model and we show that our extension
allows us to better capture morphological coherence. Our work is freely
available as part of Moses.Comment: Accepted as a long paper for ACL 201
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
Selective Attention for Context-aware Neural Machine Translation
Despite the progress made in sentence-level NMT, current systems still fall
short at achieving fluent, good quality translation for a full document. Recent
works in context-aware NMT consider only a few previous sentences as context
and may not scale to entire documents. To this end, we propose a novel and
scalable top-down approach to hierarchical attention for context-aware NMT
which uses sparse attention to selectively focus on relevant sentences in the
document context and then attends to key words in those sentences. We also
propose single-level attention approaches based on sentence or word-level
information in the context. The document-level context representation, produced
from these attention modules, is integrated into the encoder or decoder of the
Transformer model depending on whether we use monolingual or bilingual context.
Our experiments and evaluation on English-German datasets in different document
MT settings show that our selective attention approach not only significantly
outperforms context-agnostic baselines but also surpasses context-aware
baselines in most cases.Comment: Accepted at NAACL-HLT 201
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