Automatic methods for low-cost evaluation and position-aware models for neural information retrieval

An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets.Ein Information-Retrieval (IR) System hilft Menschen bei der Arbeit mit großen Datenmengen, daher ist die Entwicklung und Evaluation solcher Systeme wichtig. Allerdings gibt es zwei Herausforderungen: die große Anzahl von Anfrage-Dokument-Paaren, die manuelle IREvaluation schwierig macht; sowie die komplizierten zu modellierenden Muster, aufgrund der nicht-symmetrischen, heterogenen Beziehung zwischen einem Anfragen und Dokumenten, wo erwiesen ist dass verschiedene Interaktionsmuster wie Termabhängigkeiten und Termnähe wichtig sind, aber nicht einfach durch ein einzelnes IR-Modell zu erfassen sind. In dieser Dissertation versuchen wir, beide Herausforderungen aus der Perspektive der IR-Evaluation bzw. der IR-Modellierung anzugehen, indem wir die manuellen Kosten mit automatischen Methoden reduzieren, indem wir die Verwendung von Crowdsourcing bei der Erfassung von Präferenzbewertungen untersuchen und indem wir neue neuronale IR-Modelle vorschlagen. Um die große Anzahl von Anfrage-Dokument-Paaren in der IR-Evaluation in Angriff zu nehmen, schlagen wir eine kostengünstige selektive Bewertungsmethode vor, die nur eine kleine Untermenge von repräsentativen Dokumenten für manuelle Beurteilungen auswählt, deren Ergebnisse dann extrapoliert werden; darüber hinaus wird ein unüberwachtes sprachmodellbasiertes Gütemaß für Neuheit und Diversität vorgeschlagen, wobei der Inhalt der bewerteten Dokumente genutzt wird, um unvollständige Bewertungen zu kompensieren. Außerdem versuchen wir Präferenzbewertungen praktisch nutzbar zu machen, indem wir empirisch verschiedene Eigenschaften der Bewertungen beim Sammeln über Crowdsourcing untersuchen, und indem wir einen neuartigen Bewertungsmechanismus entwickeln, der einen Kompromiss zwischen der Bewertungsqualität und der Anzahl der Bewertungen macht. Abschließend, um verschiedene komplizierte Muster in einem einzigen IR-Modell zu erfassen, inspiriert von den jüngsten Fortschritten bei Deep-Learning-Verfahren, entwickeln wir neuartige neuronale IR-Modelle, die verschiedene Muster wie Termabhängigkeit, Termnähe, Relevanzdichte sowie Anfrageabdeckung in einem einzelnen IR-Modell integrieren. Experimente auf verschiedenen Datensätzen zeigen die überlegene Performance des vorgeschlagenen IR-Modells

Hashing as Tie-Aware Learning to Rank

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.Comment: 15 pages, 3 figures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

A Novel ILP Framework for Summarizing Content with High Lexical Variety

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.Comment: Accepted for publication in the journal of Natural Language Engineering, 201