63,944 research outputs found
Comparative evaluation of research vs. Online MT systems
This paper reports MT evaluation experiments that were conducted at the end of year 1 of the EU-funded CoSyne
1 project for three language combinations, considering translations from German, Italian and Dutch into English. We present a comparative evaluation of the MT software developed within the project against four of the leading free webbased MT systems across a range of state-of-the-art automatic evaluation metrics. The data sets from the news domain that were created and used for training purposes and also for this evaluation exercise, which are available to the research community, are also described. The evaluation results for the news domain are very encouraging: the CoSyne MT software consistently beats the rule-based MT systems, and for translations from Italian and Dutch into English in particular the scores given by some of the standard automatic evaluation metrics are not too distant from those obtained by wellestablished statistical online MT systems
Transfer Learning for Multi-language Twitter Election Classification
Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
We evaluate a semantic parser based on a character-based sequence-to-sequence
model in the context of the SemEval-2017 shared task on semantic parsing for
AMRs. With data augmentation, super characters, and POS-tagging we gain major
improvements in performance compared to a baseline character-level model.
Although we improve on previous character-based neural semantic parsing models,
the overall accuracy is still lower than a state-of-the-art AMR parser. An
ensemble combining our neural semantic parser with an existing, traditional
parser, yields a small gain in performance.Comment: To appear in Proceedings of SemEval, 2017 (camera-ready
Dublin City University at CLEF 2007: Cross-Language Speech Retrieval Experiments
The Dublin City University participation in the CLEF 2007 CL-SR English task concentrated primarily on issues of topic translation. Our retrieval system used the BM25F model and pseudo relevance feedback. Topics were translated into English using the Yahoo! BabelFish free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domainspecific translation lexicons can provide improved CLIR effectiveness for this task
Multilingual search for cultural heritage archives via combining multiple translation resources
The linguistic features of material in Cultural Heritage (CH) archives may be in various languages requiring a facility for effective multilingual search. The specialised
language often associated with CH content introduces problems for automatic translation to support search applications. The MultiMatch project is focused on enabling
users to interact with CH content across different media types and languages. We present results from a MultiMatch study exploring various translation techniques for
the CH domain. Our experiments examine translation techniques for the English language CLEF 2006 Cross-Language
Speech Retrieval (CL-SR) task using Spanish, French and German queries. Results compare effectiveness of our query
translation against a monolingual baseline and show improvement when combining a domain-specific translation lexicon with a standard machine translation system
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
In this paper, we propose a novel deep generative approach to cross-modal
retrieval to learn hash functions in the absence of paired training samples
through the cycle consistency loss. Our proposed approach employs adversarial
training scheme to lean a couple of hash functions enabling translation between
modalities while assuming the underlying semantic relationship. To induce the
hash codes with semantics to the input-output pair, cycle consistency loss is
further proposed upon the adversarial training to strengthen the correlations
between inputs and corresponding outputs. Our approach is generative to learn
hash functions such that the learned hash codes can maximally correlate each
input-output correspondence, meanwhile can also regenerate the inputs so as to
minimize the information loss. The learning to hash embedding is thus performed
to jointly optimize the parameters of the hash functions across modalities as
well as the associated generative models. Extensive experiments on a variety of
large-scale cross-modal data sets demonstrate that our proposed method achieves
better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text
overlap with arXiv:1703.10593 by other author
Learning to generate one-sentence biographies from Wikidata
We investigate the generation of one-sentence Wikipedia biographies from
facts derived from Wikidata slot-value pairs. We train a recurrent neural
network sequence-to-sequence model with attention to select facts and generate
textual summaries. Our model incorporates a novel secondary objective that
helps ensure it generates sentences that contain the input facts. The model
achieves a BLEU score of 41, improving significantly upon the vanilla
sequence-to-sequence model and scoring roughly twice that of a simple template
baseline. Human preference evaluation suggests the model is nearly as good as
the Wikipedia reference. Manual analysis explores content selection, suggesting
the model can trade the ability to infer knowledge against the risk of
hallucinating incorrect information
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