1,763 research outputs found
Modeling Target-Side Inflection in Neural Machine Translation
NMT systems have problems with large vocabulary sizes. Byte-pair encoding
(BPE) is a popular approach to solving this problem, but while BPE allows the
system to generate any target-side word, it does not enable effective
generalization over the rich vocabulary in morphologically rich languages with
strong inflectional phenomena. We introduce a simple approach to overcome this
problem by training a system to produce the lemma of a word and its
morphologically rich POS tag, which is then followed by a deterministic
generation step. We apply this strategy for English-Czech and English-German
translation scenarios, obtaining improvements in both settings. We furthermore
show that the improvement is not due to only adding explicit morphological
information.Comment: Accepted as a research paper at WMT17. (Updated version with
corrected references.
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
This paper presents the submissions by the University of Zurich to the
SIGMORPHON 2017 shared task on morphological reinflection. The task is to
predict the inflected form given a lemma and a set of morpho-syntactic
features. We focus on neural network approaches that can tackle the task in a
limited-resource setting. As the transduction of the lemma into the inflected
form is dominated by copying over lemma characters, we propose two recurrent
neural network architectures with hard monotonic attention that are strong at
copying and, yet, substantially different in how they achieve this. The first
approach is an encoder-decoder model with a copy mechanism. The second approach
is a neural state-transition system over a set of explicit edit actions,
including a designated COPY action. We experiment with character alignment and
find that naive, greedy alignment consistently produces strong results for some
languages. Our best system combination is the overall winner of the SIGMORPHON
2017 Shared Task 1 without external resources. At a setting with 100 training
samples, both our approaches, as ensembles of models, outperform the next best
competitor.Comment: To appear in Proceedings of the 15th Annual SIGMORPHON Workshop on
Computational Research in Phonetics, Phonology, and Morphology at CoNLL 201
TermEval: an automatic metric for evaluating terminology translation in MT
Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain-knowledge from source to target is arguably the most concerning factor for the customers in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. However, evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem,
which could aid the end-users to instantly identify term translation problems in MT.
In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold standard evaluation test set, we semi-automatically create a gold-standard dataset from English--Hindi judicial domain parallel corpus.
We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations
Evaluation of Croatian Word Embeddings
Croatian is poorly resourced and highly inflected language from Slavic
language family. Nowadays, research is focusing mostly on English. We created a
new word analogy corpus based on the original English Word2vec word analogy
corpus and added some of the specific linguistic aspects from Croatian
language. Next, we created Croatian WordSim353 and RG65 corpora for a basic
evaluation of word similarities. We compared created corpora on two popular
word representation models, based on Word2Vec tool and fastText tool. Models
has been trained on 1.37B tokens training data corpus and tested on a new
robust Croatian word analogy corpus. Results show that models are able to
create meaningful word representation. This research has shown that free word
order and the higher morphological complexity of Croatian language influences
the quality of resulting word embeddings.Comment: In review process on LREC 2018 conferenc
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
The necessity of using a fixed-size word vocabulary in order to control the
model complexity in state-of-the-art neural machine translation (NMT) systems
is an important bottleneck on performance, especially for morphologically rich
languages. Conventional methods that aim to overcome this problem by using
sub-word or character-level representations solely rely on statistics and
disregard the linguistic properties of words, which leads to interruptions in
the word structure and causes semantic and syntactic losses. In this paper, we
propose a new vocabulary reduction method for NMT, which can reduce the
vocabulary of a given input corpus at any rate while also considering the
morphological properties of the language. Our method is based on unsupervised
morphology learning and can be, in principle, used for pre-processing any
language pair. We also present an alternative word segmentation method based on
supervised morphological analysis, which aids us in measuring the accuracy of
our model. We evaluate our method in Turkish-to-English NMT task where the
input language is morphologically rich and agglutinative. We analyze different
representation methods in terms of translation accuracy as well as the semantic
and syntactic properties of the generated output. Our method obtains a
significant improvement of 2.3 BLEU points over the conventional vocabulary
reduction technique, showing that it can provide better accuracy in open
vocabulary translation of morphologically rich languages.Comment: The 20th Annual Conference of the European Association for Machine
Translation (EAMT), Research Paper, 12 page
Domain adaptation strategies in statistical machine translation: a brief overview
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft
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