5,828 research outputs found
Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking
Re-speaking is a mechanism for obtaining high quality subtitles for use in
live broadcast and other public events. Because it relies on humans performing
the actual re-speaking, the task of estimating the quality of the results is
non-trivial. Most organisations rely on humans to perform the actual quality
assessment, but purely automatic methods have been developed for other similar
problems, like Machine Translation. This paper will try to compare several of
these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER and RIBES. These will
then be matched to the human-derived NER metric, commonly used in re-speaking.Comment: Comparison and Adaptation of Automatic Evaluation Metrics for Quality
Assessment of Re-Speaking. arXiv admin note: text overlap with
arXiv:1509.0908
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Mutual terminology extraction using a statistical framework
In this paper, we explore a statistical framework for mutual bilingual terminology extraction. We propose three probabilistic models to assess the proposition that automatic alignment can play an active role in bilingual terminology extraction and translate it into mutual bilingual terminology extraction. The results indicate that such models are valid and can show that mutual bilingual terminology extraction is indeed a viable approach
UGENT-LT3 SCATE Submission for WMT16 Shared Task on Quality Estimation
This paper describes the submission of the UGENT-LT3 SCATE system to the WMT16 Shared Task on Quality Estimation (QE), viz. English-German word and sentence-level QE. Based on the observation that the data set is homogeneous (all sentences belong to the IT domain), we performed bilingual terminology extraction and added features derived from the resulting term list to the well-performing features of the word-level QE task of last year. For sentence-level QE, we analyzed the importance of the features and based on those insights extended the feature set of last year. We also experimented with different learning methods and ensembles. We present our observations from the different experiments we conducted and our submissions for both tasks
A Data-Oriented Model of Literary Language
We consider the task of predicting how literary a text is, with a gold
standard from human ratings. Aside from a standard bigram baseline, we apply
rich syntactic tree fragments, mined from the training set, and a series of
hand-picked features. Our model is the first to distinguish degrees of highly
and less literary novels using a variety of lexical and syntactic features, and
explains 76.0 % of the variation in literary ratings.Comment: To be published in EACL 2017, 11 page
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
462 Machine Translation Systems for Europe
We built 462 machine translation systems for all language pairs of the Acquis Communautaire corpus. We report and analyse the performance of these system, and compare them against pivot translation and a number of system combination methods (multi-pivot, multisource) that are possible due to the available systems.JRC.G.2-Global security and crisis managemen
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