Représentation à base de connaissance pour une méthode de classification transductive de document multilangue

International audienceMultilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification

Cross-lingual Distillation for Text Classification

Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available. An adversarial feature adaptation technique is also applied during the model training to reduce distribution mismatch. We conducted experiments on two benchmark CLTC datasets, treating English as the source language and German, French, Japan and Chinese as the unlabeled target languages. The proposed approach had the advantageous or comparable performance of the other state-of-art methods.Comment: Accepted at ACL 2017; Code available at https://github.com/xrc10/cross-distil

Leveraging literals for knowledge graph embeddings

Wissensgraphen (Knowledge Graphs, KGs) repräsentieren strukturierte Fakten, die sich aus Entitäten und den zwischen diesen bestehenden Relationen zusammensetzen. Um die Effizienz von KG-Anwendungen zu maximieren, ist es von Vorteil, KGs in einen niedrigdimensionalen Vektorraum zu transformieren. KGs folgen dem Paradigma einer offenen Welt (Open World Assumption, OWA), d. h. fehlende Information wird als potenziell möglich angesehen, wodurch ihre Verwendung in realen Anwendungsszenarien oft eingeschränkt wird. Link-Vorhersage (Link Prediction, LP) zur Vervollständigung von KGs kommt daher eine hohe Bedeutung zu. LP kann in zwei unterschiedlichen Modi durchgeführt werden, transduktiv und induktiv, wobei die erste Möglichkeit voraussetzt, dass alle Entitäten der Testdaten in den Trainingsdaten vorhanden sind, während die zweite Möglichkeit auch zuvor nicht bekannte Entitäten in den Testdaten zulässt. Die vorliegende Arbeit untersucht die Verwendung von Literalen in der transduktiven und induktiven LP, da KGs zahlreiche numerische und textuelle Literale enthalten, die eine wesentliche Semantik aufweisen. Zur Evaluierung dieser LP Methoden werden spezielle Benchmark-Datensätze eingeführt. Insbesondere wird eine neuartige KG Embedding (KGE) Methode, RAILD, vorgeschlagen, die Textliterale zusammen mit kontextuellen Graphinformationen für die LP nutzt. Das Ziel von RAILD ist es, die bestehende Forschungslücke beim Lernen von Embeddings für beim Training ungesehene Relationen zu schließen. Dafür wird eine Architektur vorgeschlagen, die Sprachmodelle (Language Models, LMs) mit Netzwerkembeddings kombiniert. Hierzu erfolgt ein Feintuning von leistungsstarken vortrainierten LMs wie BERT zum Zweck der LP, wobei textuelle Beschreibungen von Entitäten und Relationen genutzt werden. Darüber hinaus wird ein neuer Algorithmus, WeiDNeR, eingeführt, um ein Relationsnetzwerk zu generieren, das zum Erlernen graphbasierter Embeddings von Relationen unter Verwendung eines Netzwerkembeddingsmodells dient. Die Vektorrepräsentationen dieser Relationen werden für die LP kombiniert. Zudem wird ein weiteres neuartiges Embeddingmodell, LitKGE, vorgestellt, das numerische Literale für die transduktive LP verwendet. Es zielt darauf ab, numerische Merkmale für Entitäten durch Graphtraversierung zu erzeugen. Hierfür wird ein weiterer Algorithmus, WeiDNeR_Extended, eingeführt, der ein Netzwerk aus Objekt- und Datentypproperties erzeugt. Aus den aus diesem Netzwerk extrahierten Propertypfaden werden dann numerische Merkmale von Entitäten generiert. Des Weiteren wird der Einsatz eines mehrsprachigen LM zur Kodierung von Entitätenbeschreibungen in verschiedenen natürlichen Sprachen zum Zweck der LP untersucht. Für die Evaluierung der KGE-Modelle wurden die Benchmark-Datensätze LiterallyWikidata und Wikidata68K erstellt. Die vielversprechenden Ergebnisse, die mit den vorgestellten Modellen erzielt wurden, eröffnen interessante Fragestellungen für die zukünftige Forschung auf dem Gebiet der KGEs und ihrer Folgeanwendungen

Transductive Auxiliary Task Self-Training for Neural Multi-Task Models

Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task self-training improves absolute accuracy by up to 9.56% over the pure multi-task model for dependency relation tagging and by up to 13.03% for semantic tagging.Comment: Camera ready version, to appear at DeepLo 2019 (EMNLP workshop

Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models

Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few-shot classification. Few-shot classification involves training a model to perform a new classification task with a handful of labeled data. This paper presents three contributions. First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints. Second, we propose a transductive inference, a learning paradigm that has been overlooked by the NLP community. Transductive inference, unlike traditional inductive learning, leverages the statistics of unlabeled data. We also introduce a new parameter-free transductive regularizer based on the Fisher-Rao loss, which can be used on top of the gated API embeddings. This method fully utilizes unlabeled data, does not share any label with the third-party API provider and could serve as a baseline for future research. Third, we propose an improved experimental setting and compile a benchmark of eight datasets involving multiclass classification in four different languages, with up to 151 classes. We evaluate our methods using eight backbone models, along with an episodic evaluation over 1,000 episodes, which demonstrate the superiority of transductive inference over the standard inductive setting.Comment: EMNLP 202

Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT

Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.Comment: EMNLP 2019 Camera Read

Transfer Learning in Natural Language Processing through Interactive Feedback

Machine learning models cannot easily adapt to new domains and applications. This drawback becomes detrimental for natural language processing (NLP) because language is perpetually changing. Across disciplines and languages, there are noticeable differences in content, grammar, and vocabulary. To overcome these shifts, recent NLP breakthroughs focus on transfer learning. Through clever optimization and engineering, a model can successfully adapt to a new domain or task. However, these modifications are still computationally inefficient or resource-intensive. Compared to machines, humans are more capable at generalizing knowledge across different situations, especially in low-resource ones. Therefore, the research on transfer learning should carefully consider how the user interacts with the model. The goal of this dissertation is to investigate “human-in-the-loop” approaches for transfer learning in NLP. First, we design annotation frameworks for inductive transfer learning, which is the transfer of models across tasks. We create an interactive topic modeling system for users to find topics useful for classifying documents in multiple languages. The user-constructed topic model bridges improves classification accuracy and bridges cross-lingual gaps in knowledge. Next, we look at popular language models, like BERT, that can be applied to various tasks. While these models are useful, they still require a large amount of labeled data to learn a new task. To reduce labeling, we develop an active learning strategy which samples documents that surprise the language model. Users only need to annotate a small subset of these unexpected documents to adapt the language model for text classification. Then, we transition to user interaction in transductive transfer learning, which is the transfer of models across domains. We focus our efforts on low-resource languages to develop an interactive system for word embeddings. In this approach, the feedback from bilingual speakers refines the cross-lingual embedding space for classification tasks. Subsequently, we look at domain shift for tasks beyond text classification. Coreference resolution is fundamental for NLP applications, like question-answering and dialogue, but the models are typically trained and evaluated on one dataset. We use active learning to find spans of text in the new domain for users to label. Furthermore, we provide important insights on annotating spans for domain adaptation. Finally, we summarize the contributions of each chapter. We focus on aspects like the scope of applications and model complexity. We conclude with a discussion of future directions. Researchers may extend the ideas in our thesis to topics like user-centric active learning and proactive learning