Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension

Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network `skill' transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase detection and question type classification into the reading comprehension model. We conduct an empirical evaluation and show that transferring language skill knowledge leads to significant improvements for the task with much fewer steps compared to the baseline model. We also show that the skill transfer approach is effective even with small amounts of training data. Another finding of this work is that using token-wise deep label supervision for text classification improves the performance of transfer learning

Learning and evaluating the content and structure of a term taxonomy

Journal ArticleIn this paper, we describe a weakly supervised bootstrapping algorithm that reads Web texts and learns taxonomy terms. The bootstrapping algorithm starts with two seed words (a seed hypernym (Root concept) and a seed hyponym) that are inserted into a doubly anchored hyponym pattern. In alternating rounds, the algorithm learns new hyponym terms and new hypernym terms that are subordinate to the Root concept. We conducted an extensive evaluation with human annotators to evaluate the learned hyponym and hypernym terms for two categories: animals and people

Semantic class learning from the web with hyponym pattern linkage graphs

Journal ArticleWe present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances. Together, these two measures capture not only frequency of occurrence, but also cross-checking that the candidate occurs both near the class name and near other class members. We developed two algorithms that begin with just a class name and one seed instance and then automatically generate a ranked list of new class instances. We conducted experiments on four semantic classes and consistently achieved high accuracies

L’aspect grammatical et ses manifestations dans les traductions en français de textes littéraires bulgares

This dissertation is a reflection on the linguistic and ethical issues at play in translation, hinging on an examination of the temporal and aspectual values in the French and Bulgarian languages. The question of aspect in Bulgarian is approached through the study of its possible translations in French.The orientations of this work go along the belief that translation plays a crucial role in the understanding of the mechanisms underlying the functioning of languages.The theoretical approach of this dissertation made it necessary to describe the following temporal paradigms: the aorist and perfect in Bulgarian, the “passé simple”, “imparfait” and “passé composé” in French. It also considers and illustrates some orthonymic devices so as to underline the importance of the referential experience in the craft of writing a translation, and also to highlight the omnipresence, in the translator’s mind, of a conception of what is natural and appropriate to say in a given language. Going through the notions of tense and aspect made it possible to shed light on the handling of the secondary imperfective forms, which proves to be essential for the comprehension of aspect. Moreover, even though the original text and the translation give different perspectives on the action, preferring one interpretation or the other does not obstruct the reader’s understanding. These divergences prove that the existence of aspectual oppositions in Bulgarian are hardly ever taken into account when translated. The examination of the perfect led to the broadening of our study to the analysis of meditative values, the expression of which, in Bulgarian, is strongly integrated in the morphology of the verbs.Dans ce travail, la réflexion sur les enjeux linguistiques (et éthiques) de la traduction s’articule à l’étude de valeurs temporelles et aspectuelles en bulgare et en français. La question de l’aspectualité en bulgare a été envisagée sous l’angle de la possibilité de ses manifestations en français. Les orientations principales de cette étude ont été guidées par la conviction du rôle fondamental de la traduction pour la compréhension des mécanismes régissant les langues. L’approche théorique a nécessité la description de paradigmes temporels : l’aoriste et le parfait en bulgare, le passé simple, l’imparfait et le passé composé en français. Un certain nombre de procédés orthonymiques ont été considérés et illustrés afin de souligner l’importance, lors de la constitution de l’écriture de la traduction, de l’expérience référentielle, et de l’omniprésence, dans l’esprit des traducteurs, d’une conception jugée correcte et naturelle de s’exprimer. La revue des notions théoriques autour du temps et de l’aspect a permis de mettre en lumière le traitement des imperfectifs secondaires, fondamental pour l’appréhension de l’aspect. Texte original et traduction attestent également de différentes visions des procès mais le choix de l’une ou de l’autre représentation ne constitue pas un obstacle à la réception sans aspérités du texte traduit. Cette divergence de représentations démontre que l’existence d’oppositions aspectuelles en bulgare est rarement prise en compte par le traducteur. L’examen du parfait a constitué une ouverture vers l’analyse de valeurs médiatives dont l’expression est, dans une langue comme le bulgare, fortement intégrée dans la morphologie verbale

Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary