7 research outputs found
Findings of the 2014 Workshop on Statistical Machine Translation
This paper presents the results of the
WMT14 shared tasks, which included a
standard news translation task, a separate
medical translation task, a task for
run-time estimation of machine translation
quality, and a metrics task. This year, 143
machine translation systems from 23 institutions
were submitted to the ten translation
directions in the standard translation
task. An additional 6 anonymized systems
were included, and were then evaluated
both automatically and manually. The
quality estimation task had four subtasks,
with a total of 10 teams, submitting 57 entries
Findings of the 2015 Workshop on Statistical Machine Translation
This paper presents the results of the
WMT15 shared tasks, which included a
standard news translation task, a metrics
task, a tuning task, a task for run-time
estimation of machine translation quality,
and an automatic post-editing task. This
year, 68 machine translation systems from
24 institutions were submitted to the ten
translation directions in the standard translation
task. An additional 7 anonymized
systems were included, and were then
evaluated both automatically and manually.
The quality estimation task had three
subtasks, with a total of 10 teams, submitting
34 entries. The pilot automatic postediting
task had a total of 4 teams, submitting
7 entries
Parallel FDA5 for fast deployment of accurate statistical machine translation systems
We use parallel FDA5, an efficiently parameterized and optimized parallel implementation of feature decay algorithms for fast deployment of accurate statistical
machine translation systems, taking only about half a day for each translation direction.
We build Parallel FDA5 Moses SMT systems for all language pairs in the WMT14 translation task and obtain SMT
performance close to the top Moses systems with an average of BLEU points difference using significantly less resources for training and development
Results of the WMT17 metrics shared task
This paper presents the results of the
WMT17 Metrics Shared Task. We asked
participants of this task to score the outputs of the MT systems involved in the
WMT17 news translation task and Neural MT training task. We collected scores
of 14 metrics from 8 research groups. In
addition to that, we computed scores of
7 standard metrics (BLEU, SentBLEU,
NIST, WER, PER, TER and CDER) as
baselines. The collected scores were evaluated in terms of system-level correlation
(how well each metricâs scores correlate
with WMT17 official manual ranking of
systems) and in terms of segment level
correlation (how often a metric agrees with
humans in judging the quality of a particular sentence).
This year, we build upon two types of
manual judgements: direct assessment
(DA) and HUME manual semantic judgements
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic
difficulty in parallelizing their state computations. In this work, we propose
the Simple Recurrent Unit (SRU), a light recurrent unit that balances model
capacity and scalability. SRU is designed to provide expressive recurrence,
enable highly parallelized implementation, and comes with careful
initialization to facilitate training of deep models. We demonstrate the
effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over
cuDNN-optimized LSTM on classification and question answering datasets, and
delivers stronger results than LSTM and convolutional models. We also obtain an
average of 0.7 BLEU improvement over the Transformer model on translation by
incorporating SRU into the architecture.Comment: EMNL
From Word Embeddings to Large Vocabulary Neural Machine Translation
Dans ce mémoire, nous examinons certaines propriétés
des représentations distribuées de mots et nous proposons une technique
pour Ă©largir le vocabulaire des systĂšmes de traduction automatique neurale.
En premier lieu, nous considérons un problÚme de résolution d'analogies
bien connu et examinons l'effet de poids adaptés à la position, le choix de la
fonction de combinaison et l'impact de l'apprentissage supervisé.
Nous enchaßnons en montrant que des représentations distribuées simples basées
sur la traduction peuvent atteindre ou dépasser l'état de l'art sur le test de
détection de synonymes TOEFL et sur le récent étalon-or SimLex-999. Finalament,
motivé par d'impressionnants résultats obtenus avec des représentations distribuées
issues de systĂšmes de traduction neurale Ă petit vocabulaire (30 000 mots),
nous présentons une approche compatible à l'utilisation de cartes graphiques
pour augmenter la taille du vocabulaire par plus d'un ordre de magnitude.
Bien qu'originalement développée seulement pour obtenir les représentations
distribuées, nous montrons que cette technique fonctionne plutÎt bien sur des
tùches de traduction, en particulier de l'anglais vers le français (WMT'14).In this thesis, we examine some properties of word embeddings
and propose a technique to handle large vocabularies in neural
machine translation. We first look at a well-known analogy task
and examine the effect of position-dependent weights, the choice
of combination function and the impact of supervised learning.
We then show that
simple embeddings learnt with translational contexts can match or surpass
the state of the art on the TOEFL synonym detection task and on
the recently introduced SimLex-999 word similarity gold standard. Finally,
motivated by impressive results obtained by small-vocabulary (30,000 words)
neural machine translation embeddings on some word similarity tasks, we
present a GPU-friendly approach to increase the vocabulary size
by more than an order of magnitude. Despite originally being developed for
obtaining the embeddings only, we show that this technique
actually works quite well on actual translation tasks, especially
for English to French (WMT'14)