BIKE: Bilingual Keyphrase Experiments

This paper presents a novel strategy for translating lists of keyphrases. Typical keyphrase lists appear in scientific articles, information retrieval systems and web page meta-data. Our system combines a statistical translation model trained on a bilingual corpus of scientific papers with sense-focused look-up in a large bilingual terminological resource. For the latter, we developed a novel technique that benefits from viewing the keyphrase list as contextual help for sense disambiguation. The optimal combination of modules was discovered by a genetic algorithm. Our work applies to the French / English language pair

UGENT-LT3 SCATE system for machine translation quality estimation

This paper describes the submission of the UGENT-LT3 SCATE system to the WMT15 Shared Task on Quality Estima-tion (QE), viz. English-Spanish word and sentence-level QE. We conceived QE as a supervised Machine Learning (ML) problem and designed additional features and combined these with the baseline feature set to estimate quality. The sen-tence-level QE system re-uses the word level predictions of the word-level QE system. We experimented with different learning methods and observe improve-ments over the baseline system for word-level QE with the use of the new features and by combining learning methods into ensembles. For sentence-level QE we show that using a single feature based on word-level predictions can perform better than the baseline system and using this in combination with additional features led to further improvements in performance

Sentence Alignment using MR and GA

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on mathematical regression (MR) and genetic algorithm (GA) classifiers are presented. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the mathematical regression and genetic algorithm models. Another set of data was used for testing. The results of (MR) and (GA) outperform the results of length based approach. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research

Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance

We introduce a new measure of distance between languages based on word embedding, called word embedding language divergence (WELD). WELD is defined as divergence between unified similarity distribution of words between languages. Using such a measure, we perform language comparison for fifty natural languages and twelve genetic languages. Our natural language dataset is a collection of sentence-aligned parallel corpora from bible translations for fifty languages spanning a variety of language families. Although we use parallel corpora, which guarantees having the same content in all languages, interestingly in many cases languages within the same family cluster together. In addition to natural languages, we perform language comparison for the coding regions in the genomes of 12 different organisms (4 plants, 6 animals, and two human subjects). Our result confirms a significant high-level difference in the genetic language model of humans/animals versus plants. The proposed method is a step toward defining a quantitative measure of similarity between languages, with applications in languages classification, genre identification, dialect identification, and evaluation of translations

A competence-performance based model to develop a syntactic language for artificial agents

The hypothesis of language use is an attractive theory in order to explain how natural languages evolve and develop in social populations. In this paper we present a model partially based on the idea of language games, so that a group of artificial agents are able to produce and share a symbolic language with syntactic structure. Grammatical structure is induced by grammatical evolution of stochastic regular grammars with learning capabilities, while language development is refined by means of language games where the agents apply on-line probabilistic reinforcement learning. Within this framework, the model adapts the concepts of competence and performance in language, as they have been proposed in some linguistic theories. The first experiments in this article have been organized around the linguistic description of visual scenes with the possibility of changing the referential situations. A second and more complicated experimental setting is also analyzed, where linguistic descriptions are enforced to keep word order constraints.The second author has been supported by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL)

Data-driven sentence simplification: Survey and benchmark

Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand. In order to do so, several rewriting transformations can be performed such as replacement, reordering, and splitting. Executing these transformations while keeping sentences grammatical, preserving their main idea, and generating simpler output, is a challenging and still far from solved problem. In this article, we survey research on SS, focusing on approaches that attempt to learn how to simplify using corpora of aligned original-simplified sentence pairs in English, which is the dominant paradigm nowadays. We also include a benchmark of different approaches on common datasets so as to compare them and highlight their strengths and limitations. We expect that this survey will serve as a starting point for researchers interested in the task and help spark new ideas for future developments