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Phrase-level System Combination for Machine Translation Based on Target-to-Target Decoding
In this paper, we propose a novel lattice-based MT combination methodology that we call Target-to-Target Decoding (TTD). The combination process is carried out as a “translation” from backbone to the combination result. This perspective suggests the use of existing phrase-based MT techniques in the combination framework. We show how phrase extraction rules and confidence estimations inspired from machine translation improve results. We also propose system-specific LMs for estimating N-gram consensus. Our results show that our approach yields a strong improvement over the best single MT system and competes with other state-of-the-art combination systems
Marker-based filtering of bilingual phrase pairs for SMT
State-of-the-art statistical machine translation
systems make use of a large translation table obtained after scoring a set of bilingual phrase pairs automatically extracted from a parallel corpus. The number of bilingual phrase pairs extracted from a pair of aligned sentences grows exponentially as the length of the sentences increases; therefore, the number of entries in the phrase table used to carry out the translation may become unmanageable, especially when online, 'on demand' translation is required in real time. We describe
the use of closed-class words to filter the set of bilingual phrase pairs extracted from the parallel corpus by taking into account the alignment information
and the type of the words involved in the alignments. On four European language pairs, we show that our simple yet novel approach can filter the phrase table by up to
a third yet still provide competitive results compared to the baseline. Furthermore, it provides a nice balance between the unfiltered approach and pruning using stop
words, where the deterioration in translation quality is unacceptably high
System Combination via Quality Estimation for Grammatical Error Correction
Quality estimation models have been developed to assess the corrections made
by grammatical error correction (GEC) models when the reference or
gold-standard corrections are not available. An ideal quality estimator can be
utilized to combine the outputs of multiple GEC systems by choosing the best
subset of edits from the union of all edits proposed by the GEC base systems.
However, we found that existing GEC quality estimation models are not good
enough in differentiating good corrections from bad ones, resulting in a low
F0.5 score when used for system combination. In this paper, we propose GRECO, a
new state-of-the-art quality estimation model that gives a better estimate of
the quality of a corrected sentence, as indicated by having a higher
correlation to the F0.5 score of a corrected sentence. It results in a combined
GEC system with a higher F0.5 score. We also propose three methods for
utilizing GEC quality estimation models for system combination with varying
generality: model-agnostic, model-agnostic with voting bias, and
model-dependent method. The combined GEC system outperforms the state of the
art on the CoNLL-2014 test set and the BEA-2019 test set, achieving the highest
F0.5 scores published to date.Comment: EMNLP 202
Identifying Semantic Divergences in Parallel Text without Annotations
Recognizing that even correct translations are not always semantically
equivalent, we automatically detect meaning divergences in parallel sentence
pairs with a deep neural model of bilingual semantic similarity which can be
trained for any parallel corpus without any manual annotation. We show that our
semantic model detects divergences more accurately than models based on surface
features derived from word alignments, and that these divergences matter for
neural machine translation.Comment: Accepted as a full paper to NAACL 201
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
Findings of the IWSLT 2022 Evaluation Campaign.
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved
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
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