In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

Validating multilingual hybrid automatic term extraction for search engine optimisation : the use case of EBM-GUIDELINES

Tools that automatically extract terms and their equivalents in other languages from parallel corpora can contribute to multilingual professional communication in more than one way. By means of a use case with data from a medical web site with point of care evidence summaries (Ebpracticenet), we illustrate how hybrid multilingual automatic term extraction from parallel corpora works and how it can be used in a practical application such as search engine optimisation. The original aim was to use the result of the extraction to improve the recall of a search engine by allowing automated multilingual searches. Two additional possible applications were found while considering the data: searching via related forms and searching via strongly semantically related words. The second stage of this research was to find the most suitable format for the required manual validation of the raw extraction results and to compare the validation process when performed by a domain expert versus a terminologist

Bilingual Lexicon Extraction with Temporal Distributed Word Representation from Comparable Corpora

Abstract. Distributed word representation has been found to be highly effective to extract a bilingual lexicon from comparable corpora by a simple linear transformation. However, polysemous words often vary their meanings at different time points in the corresponding corpora. A single word representation which is learned from the whole corpora can't express the temporal change of the word meaning very well. This paper proposes a simple solution which exploits the temporal distributed word representation for polysemous words. The experimental results confirm that the proposed solution can offer better performance on the Englishto-Chinese bilingual lexicon extraction task

A survey of cross-lingual word embedding models

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.</jats:p

Learning the Optimal use of Dependency-parsing Information for Finding Translations with Comparable Corpora

Abstract Using comparable corpora to find new word translations is a promising approach for extending bilingual dictionaries (semi-) automatically. The basic idea is based on the assumption that similar words have similar contexts across languages. The context of a word is often summarized by using the bag-of-words in the sentence, or by using the words which are in a certain dependency position, e.g. the predecessors and successors. These different context positions are then combined into one context vector and compared across languages. However, previous research makes the (implicit) assumption that these different context positions should be weighted as equally important. Furthermore, only the same context positions are compared with each other, for example the successor position in Spanish is compared with the successor position in English. However, this is not necessarily always appropriate for languages like Japanese and English. To overcome these limitations, we suggest to perform a linear transformation of the context vectors, which is defined by a matrix. We define the optimal transformation matrix by using a Bayesian probabilistic model, and show that it is feasible to find an approximate solution using Markov chain Monte Carlo methods. Our experiments demonstrate that our proposed method constantly improves translation accuracy

Bilingual distributed word representations from document-aligned comparable data

We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and contextcounting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.This work was done while Ivan Vuli c was a postdoctoral researcher at Department of Computer Science, KU Leuven supported by the PDM Kort fellowship (PDMK/14/117). The work was also supported by the SCATE project (IWT-SBO 130041) and the ERC Consolidator Grant LEXICAL: Lexical Acquisition Across Languages (648909)

A deep learning approach to bilingual lexicon induction in the biomedical domain.

BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations. RESULTS: The results show that the word-level and character-level representations each improve state-of-the-art results for BLI and biomedical translation mining. The best results are obtained by exploiting the synergy between these word-level and character-level representations in the classification model. We evaluate the models both quantitatively and qualitatively. CONCLUSIONS: Translation of domain-specific biomedical terminology benefits from the character-level representations compared to relying solely on word-level representations. It is beneficial to take a deep learning approach and learn character-level representations rather than relying on handcrafted representations that are typically used. Our combined model captures the semantics at the word level while also taking into account that specialized terminology often originates from a common root form (e.g., from Greek or Latin)

Induction de lexiques bilingues à partir de corpus comparables et parallèles

Les modèles statistiques tentent de généraliser la connaissance à partir de la fréquence des événements probabilistes présents dans les données. Si plus de données sont disponibles, les événements sont plus souvent observés et les modèles sont plus performants. Les approches du Traitement Automatique de la Langue basées sur ces modèles sont donc dépendantes de la disponibilité et de la quantité des ressources à disposition. Cette dépendance aux données touche en particulier la Traduction Automatique Statistique qui, de surcroît, requiert des ressources de type multilingue. Cette thèse rapporte quatre articles sur deux tâches qui contribuent de près à cette dépendance : l’Alignement de Documents Bilingues (ADB) et l’Induction de Lexiques Bilingues (ILB). La première publication décrit le système soumis à la tâche partagée d’ADB de la conférence WMT16. Développé sur un moteur de recherche, notre système indexe des sites web bilingues et tente d’identifier les pages anglaises-françaises qui sont en relation de traduction. L’alignement est réalisé grâce à la représentation "sac de mots" et un lexique bilingue. L’outil développé nous a permis d’évaluer plus de 1000 configurations et d’en identifier une qui fournit des performances respectables sur la tâche. Les trois autres articles concernent la tâche d’ILB. Le premier revient sur l’approche dite "standard" et propose une exploration en largeur des paramètres dans le contexte du Web Sémantique. Le deuxième article compare l’approche standard avec les plus récentes techniques basées sur les représentations interlingues de mots (embeddings en anglais) issues de réseaux de neurones. La dernière contribution reporte des performances globales améliorées sur la tâche en combinant, par reclassement supervisée, les sorties des deux types d’approches précédemment étudiées.Statistical models try to generalize knowledge starting from the frequency of probabilistic events in the data. If more data is available, events are more often observed and models are more e cient. Natural Language Processing approaches based on those models are therefore dependant on the quantity and availability of these resources. Thus, there is a permanent need for generating and updating the learning data. This dependency touches Statistical Machine Translation, which requires multilingual resources. This thesis refers to four articles tackling two tasks that contribute signi - cantly to this dependency: the Bilingual Documents Alignment (BDA) and the Bilingual Lexicons Induction (BLI). The rst publication describes the system submitted for the BDA shared task of the WMT16 conference. Developed on a search engine, our system indexes bilingual web sites and tries to identify the English-French pages linked by translation. The alignment is realized using a "bag of words" representation and a bilingual lexicon. The tool we have developed allowed us to evaluate more than 1,000 con gurations and identify one yielding decent performances on this particular task. The three other articles are concerned with the BLI task. The rst one focuses on the so-called standard approach, and proposes a breadth parameter exploration in the Semantic Web context. The second article compares the standard approach with more recent techniques based on interlingual representation of words, or the so-called embeddings, issued from neural networks. The last contribution reports the enhanced global performances on the task, combining the outputs of the two studied approaches through supervised reclassification

Coherence in Machine Translation

Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and with no access to extra-sentential context. In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as evidenced via discourse relations. For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text