The Circle of Meaning: From Translation to Paraphrasing and Back

The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection

Designing Statistical Language Learners: Experiments on Noun Compounds

The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: (i) it identifies a new class of designs by specifying an architecture for natural language analysis in which probabilities are given to semantic forms rather than to more superficial linguistic elements; and (ii) it explores the development of a mathematical theory to predict the expected accuracy of statistical language learning systems in terms of the volume of data used to train them. The theoretical work is illustrated by applying statistical language learning designs to the analysis of noun compounds. Both syntactic and semantic analysis of noun compounds are attempted using the proposed architecture. Empirical comparisons demonstrate that the proposed syntactic model is significantly better than those previously suggested, approaching the performance of human judges on the same task, and that the proposed semantic model, the first statistical approach to this problem, exhibits significantly better accuracy than the baseline strategy. These results suggest that the new class of designs identified is a promising one. The experiments also serve to highlight the need for a widely applicable theory of data requirements.Comment: PhD thesis (Macquarie University, Sydney; December 1995), LaTeX source, xii+214 page

Discourse oriented summarization

The meaning of text appears to be tightly related to intentions and circumstances. Context sensitivity of meaning is addressed by theories of discourse structure. Few attempts have been made to exploit text organization in summarization. This thesis is an exploration of what knowledge of discourse structure can do for content selection as a subtask of automatic summarization, and query-based summarization in particular. Query-based summarization is the task of answering an arbitrary user query or question by using content from potentially relevant sources. This thesis presents a general framework for discourse oriented summarization, relying on graphs to represent semantic relations in discourse, and redundancy as a special type of semantic relation. Semantic relations occur on several levels of text analysis (query-relevance, coherence, layout, etc.), and a broad range of textual features may be required to detect them. The graph-based framework facilitates combining multiple features into an integrated semantic model of the documents to summarize. Recognizing redundancy and entailment relations between text passages is particularly important when a summary is generated of multiple documents, e.g. to avoid including redundant content in a summary. For this reason, I pay particular attention to recognizing textual entailment. Within this framework, a three-fold evaluation is performed to evaluate different aspects of discourse oriented summarization. The first is a user study, measuring the effect on user appreciation of using a particular type of knowledge for query-based summarization. In this study, three presentation strategies are compared: summarization using the rhetorical structure of the source, a baseline summarization method which uses the layout of the source, and a baseline presentation method which uses no summarization but just a concise answer to the query. Results show that knowledge of the rhetorical structure not only helps to provide the necessary context for the user to verify that the summary addresses the query adequately, but also to increase the amount of relevant content. The second evaluation is a comparison of implementations of the graph-based framework which are capable of fully automatic summarization. The two variables in the experiment are the set of textual features used to model the source and the algorithm used to search a graph for relevant content. The features are based on cosine similarity, and are realized as graph representations of the source. The graph search algorithms are inspired by existing algorithms in summarization. The quality of summaries is measured using the Rouge evaluation toolkit. The best performer would have ranked first (Rouge-2) or second (Rouge-SU4) if it had participated in the DUC 2005 query-based summarization challenge. The third study is an evaluation in the context of the DUC 2006 summarization challenge, which includes readability measurements as well as various content-based evaluation metrics. The evaluated automatic discourse oriented summarization system is similar to the one described above, but uses additional features, i.e. layout and textual entailment. The system performed well on readability at the cost of content-based scores which were well below the scores of the highest ranking DUC 2006 participant. This indicates a trade-off between readable, coherent content and useful content, an issue yet to be explored. Previous research implies that theories of text organization generalize well to multimedia. This suggests that the discourse oriented summarization framework applies to summarizing multimedia as well, provided sufficient knowledge of the organization of the (multimedia) source documents is available. The last study in this thesis is an investigation of the applicability of structural relations in multimedia for generating picture-illustrated summaries, by relating summary content to picture-associated text (i.e. captions or surrounding paragraphs). Results suggest that captions are the more suitable annotation for selecting appropriate pictures. Compared to manual illustration, results of automatic pictures are similar if the manual picture is mainly decorative

The role of aspect in paraphrase operations

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Understanding the structure and meaning of Finnish texts: From corpus creation to deep language modelling

Natural Language Processing (NLP) is a cross-disciplinary field combining elements of computer science, artificial intelligence, and linguistics, with the objective of developing means for computational analysis, understanding or generation of human language. The primary aim of this thesis is to advance natural language processing in Finnish by providing more resources and investigating the most effective machine learning based practices for their use. The thesis focuses on NLP topics related to understanding the structure and meaning of written language, mainly concentrating on structural analysis (syntactic parsing) as well as exploring the semantic equivalence of statements that vary in their surface realization (paraphrase modelling). While the new resources presented in the thesis are developed for Finnish, most of the methodological contributions are language-agnostic, and the accompanying papers demonstrate the application and evaluation of these methods across multiple languages. The first set of contributions of this thesis revolve around the development of a state-of-the-art Finnish dependency parsing pipeline. Firstly, the necessary Finnish training data was converted to the Universal Dependencies scheme, integrating Finnish into this important treebank collection and establishing the foundations for Finnish UD parsing. Secondly, a novel word lemmatization method based on deep neural networks is introduced and assessed across a diverse set of over 50 languages. And finally, the overall dependency parsing pipeline is evaluated on a large number of languages, securing top ranks in two competitive shared tasks focused on multilingual dependency parsing. The overall outcome of this line of research is a parsing pipeline reaching state-of-the-art accuracy in Finnish dependency parsing, the parsing numbers obtained with the latest pre-trained language models approaching (at least near) human-level performance. The achievement of large language models in the area of dependency parsing— as well as in many other structured prediction tasks— brings up the hope of the large pre-trained language models genuinely comprehending language, rather than merely relying on simple surface cues. However, datasets designed to measure semantic comprehension in Finnish have been non-existent, or very scarce at the best. To address this limitation, and to reflect the general change of emphasis in the field towards task more semantic in nature, the second part of the thesis shifts its focus to language understanding through an exploration of paraphrase modelling. The second contribution of the thesis is the creation of a novel, large-scale, manually annotated corpus of Finnish paraphrases. A unique aspect of this corpus is that its examples have been manually extracted from two related text documents, with the objective of obtaining non-trivial paraphrase pairs valuable for training and evaluating various language understanding models on paraphrasing. We show that manual paraphrase extraction can yield a corpus featuring pairs that are both notably longer and less lexically overlapping than those produced through automated candidate selection, the current prevailing practice in paraphrase corpus construction. Another distinctive feature in the corpus is that the paraphrases are identified and distributed within their document context, allowing for richer modelling and novel tasks to be defined