Robust handling of out-of-vocabulary words in deep language processing

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2014Deep grammars handle with precision complex grammatical phenomena and are able to provide a semantic representation of their input sentences in some logic form amenable to computational processing, making such grammars desirable for advanced Natural Language Processing tasks. The robustness of these grammars still has room to be improved. If any of the words in a sentence is not present in the lexicon of the grammar, i.e. if it is an out-of-vocabulary (OOV) word, a full parse of that sentence may not be produced. Given that the occurrence of such words is inevitable, e.g. due to the property of lexical novelty that is intrinsic to natural languages, deep grammars need some mechanism to handle OOV words if they are to be used in applications to analyze unrestricted text. The aim of this work is thus to investigate ways of improving the handling of OOV words in deep grammars. The lexicon of a deep grammar is highly thorough, with words being assigned extremely detailed linguistic information. Accurately assigning similarly detailed information to OOV words calls for the development of novel approaches, since current techniques mostly rely on shallow features and on a limited window of context, while there are many cases where the relevant information is to be found in wider linguistic structure and in long-distance relations. The solution proposed here consists of a classifier, SVM-TK, that is placed between the input to the grammar and the grammar itself. This classifier can take a variety of features and assign to words deep lexical types which can then be used by the grammar when faced with OOV words. The classifier is based on support-vector machines which, through the use of kernels, allows the seamless use of features encoding linguistic structure in the classifier. This dissertation focuses on the HPSG framework, but the method can be used in any framework where the lexical information can be encoded as a word tag. As a case study, we take LX-Gram, a computational grammar for Portuguese, to improve its robustness with respect to OOV verbs. Given that the subcategorization frame of a word is a substantial part of what is encoded in an HPSG deep lexical type, the classifier takes graph encoding grammatical dependencies as features. At runtime, these dependencies are produced by a probabilistic dependency parser. The SVM-TK classifier is compared against the state-of-the-art approaches for OOV handling, which consist of using a standard POS-tagger to assign lexical types, in essence doing POS-tagging with a highly granular tagset. Results show that SVM-TK is able to improve on the state-of-the-art, with the usual data-sparseness bottleneck issues imposing this to happen when the amount of training data is large enough.As gramáticas de processamento profundo lidam de forma precisa com fenómenos linguisticos complexos e são capazes de providenciar uma representação semântica das frases que lhes são dadas, o que torna tais gramáticas desejáveis para tarefas avançadas em Processamento de Linguagem Natural. A robustez destas gramáticas tem ainda espaço para ser melhorada. Se alguma das palavras numa frase não se encontra presente no léxico da gramática (em inglês, uma palavra out-of-vocabulary, ou OOV), pode não ser possível produzir uma análise completa dessa frase. Dado que a ocorrência de tais palavras é algo inevitável, e.g. devido à novidade lexical que é intrínseca às línguas naturais, as gramáticas profundas requerem algum mecanismo que lhes permita lidar com palavras OOV de forma a que possam ser usadas para análise de texto em aplicações. O objectivo deste trabalho é então investigar formas de melhor lidar com palavras OOV numa gramática de processamento profundo. O léxico de uma gramática profunda é altamente granular, sendo cada palavra associada com informação linguística extremamente detalhada. Atribuir corretamente a palavras OOV informação linguística com o nível de detalhe adequado requer que se desenvolvam técnicas inovadoras, dado que as abordagens atuais baseiam-se, na sua maioria, em características superficiais (shallow features) e em janelas de contexto limitadas, apesar de haver muitos casos onde a informação relevante se encontra na estrutura linguística e em relações de longa distância. A solução proposta neste trabalho consiste num classificador, SVM-TK, que é colocado entre o input da gramática e a gramática propriamente dita. Este classificador aceita uma variedade de features e atribui às palavras tipos lexicais profundos que podem então ser usado pela gramática sempre que esta se depare com palavras OOV. O classificador baseia-se em máquinas de vetores de suporte (support-vector machines). Esta técnica, quando combinada com o uso de kernels, permite que o classificador use, de forma transparente, features que codificam estrutura linguística. Esta dissertação foca-se no enquadramento teórico HPSG, embora o método proposto possa ser usado em qualquer enquadramento onde a informação lexical possa ser codificada sob a forma de uma etiqueta atribuída a uma palavra. Como caso de estudo, usamos a LX-Gram, uma gramatica computacional para a língua portuguesa, e melhoramos a sua robustez a verbos OOV. Dado que a grelha de subcategorização de uma palavra é uma parte substancial daquilo que se encontra codificado num tipo lexical profundo em HPSG, o classificador usa features baseados em dependências gramaticais. No momento de execução, estas dependências são produzidas por um analisador de dependências probabilístico. O classificador SVM-TK é comparado com o estado-da-arte para a tarefa de resolução de palavras OOV, que consiste em usar um anotador morfossintático (POS-tagger) para atribuir tipos lexicais, fazendo, no fundo, anotação com um conjunto de etiquetas altamente detalhado. Os resultados mostram que o SVM-TK melhora o estado-da-arte, com os já habituais problemas de esparssez de dados fazendo com que este efeito seja notado quando a quantidade de dados de treino é suficientemente grande.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/41465/2007

Wide-coverage statistical parsing with minimalist grammars

Syntactic parsing is the process of automatically assigning a structure to a string of words, and is arguably a necessary prerequisite for obtaining a detailed and precise representation of sentence meaning. For many NLP tasks, it is sufficient to use parsers based on simple context free grammars. However, for tasks in which precision on certain relatively rare but semantically crucial constructions (such as unbounded wh-movements for open domain question answering) is important, more expressive grammatical frameworks still have an important role to play. One grammatical framework which has been conspicuously absent from journals and conferences on Natural Language Processing (NLP), despite continuing to dominate much of theoretical syntax, is Minimalism, the latest incarnation of the Transformational Grammar (TG) approach to linguistic theory developed very extensively by Noam Chomsky and many others since the early 1950s. Until now, all parsers using genuine transformational movement operations have had only narrow coverage by modern standards, owing to the lack of any wide-coverage TG grammars or treebanks on which to train statistical models. The received wisdom within NLP is that TG is too complex and insufficiently formalised to be applied to realistic parsing tasks. This situation is unfortunate, as it is arguably the most extensively developed syntactic theory across the greatest number of languages, many of which are otherwise under-resourced, and yet the vast majority of its insights never find their way into NLP systems. Conversely, the process of constructing large grammar fragments can have a salutary impact on the theory itself, forcing choices between competing analyses of the same construction, and exposing incompatibilities between analyses of different constructions, along with areas of over- and undergeneration which may otherwise go unnoticed. This dissertation builds on research into computational Minimalism pioneered by Ed Stabler and others since the late 1990s to present the first ever wide-coverage Minimalist Grammar (MG) parser, along with some promising initial experimental results. A wide-coverage parser must of course be equipped with a wide-coverage grammar, and this dissertation will therefore also present the first ever wide-coverage MG, which has analyses with a high level of cross-linguistic descriptive adequacy for a great many English constructions, many of which are taken or adapted from proposals in the mainstream Minimalist literature. The grammar is very deep, in the sense that it describes many long-range dependencies which even most other expressive wide-coverage grammars ignore. At the same time, it has also been engineered to be highly constrained, with continuous computational testing being applied to minimize both under- and over-generation. Natural language is highly ambiguous, both locally and globally, and even with a very strong formal grammar, there may still be a great many possible structures for a given sentence and its substrings. The standard approach to resolving such ambiguity is to equip the parser with a probability model allowing it to disregard certain unlikely search paths, thereby increasing both its efficiency and accuracy. The most successful parsing models are those extracted in a supervised fashion from labelled data in the form of a corpus of syntactic trees, known as a treebank. Constructing such a treebank from scratch for a different formalism is extremely time-consuming and expensive, however, and so the standard approach is to map the trees in an existing treebank into trees of the target formalism. Minimalist trees are considerably more complex than those of other formalisms, however, containing many more null heads and movement operations, making this conversion process far from trivial. This dissertation will describe a method which has so far been used to convert 56% of the Penn Treebank trees into MG trees. Although still under development, the resulting MGbank corpus has already been used to train a statistical A* MG parser, described here, which has an expected asymptotic time complexity of O(n3); this is much better than even the most optimistic worst case analysis for the formalism

Distributed Representations for Compositional Semantics

The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches --- meaning distributed representations that exploit co-occurrence statistics of large corpora --- have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.Comment: DPhil Thesis, University of Oxford, Submitted and accepted in 201

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Structured Named Entities

The names of people, locations, and organisations play a central role in language, and named entity recognition (NER) has been widely studied, and successfully incorporated, into natural language processing (NLP) applications. The most common variant of NER involves identifying and classifying proper noun mentions of these and miscellaneous entities as linear spans in text. Unfortunately, this version of NER is no closer to a detailed treatment of named entities than chunking is to a full syntactic analysis. NER, so construed, reflects neither the syntactic nor semantic structure of NE mentions, and provides insufficient categorical distinctions to represent that structure. Representing this nested structure, where a mention may contain mention(s) of other entities, is critical for applications such as coreference resolution. The lack of this structure creates spurious ambiguity in the linear approximation. Research in NER has been shaped by the size and detail of the available annotated corpora. The existing structured named entity corpora are either small, in specialist domains, or in languages other than English. This thesis presents our Nested Named Entity (NNE) corpus of named entities and numerical and temporal expressions, taken from the WSJ portion of the Penn Treebank (PTB, Marcus et al., 1993). We use the BBN Pronoun Coreference and Entity Type Corpus (Weischedel and Brunstein, 2005a) as our basis, manually annotating it with a principled, fine-grained, nested annotation scheme and detailed annotation guidelines. The corpus comprises over 279,000 entities over 49,211 sentences (1,173,000 words), including 118,495 top-level entities. Our annotations were designed using twelve high-level principles that guided the development of the annotation scheme and difficult decisions for annotators. We also monitored the semantic grammar that was being induced during annotation, seeking to identify and reinforce common patterns to maintain consistent, parsimonious annotations. The result is a scheme of 118 hierarchical fine-grained entity types and nesting rules, covering all capitalised mentions of entities, and numerical and temporal expressions. Unlike many corpora, we have developed detailed guidelines, including extensive discussion of the edge cases, in an ongoing dialogue with our annotators which is critical for consistency and reproducibility. We annotated independently from the PTB bracketing, allowing annotators to choose spans which were inconsistent with the PTB conventions and errors, and only refer back to it to resolve genuine ambiguity consistently. We merged our NNE with the PTB, requiring some systematic and one-off changes to both annotations. This allows the NNE corpus to complement other PTB resources, such as PropBank, and inform PTB-derived corpora for other formalisms, such as CCG and HPSG. We compare this corpus against BBN. We consider several approaches to integrating the PTB and NNE annotations, which affect the sparsity of grammar rules and visibility of syntactic and NE structure. We explore their impact on parsing the NNE and merged variants using the Berkeley parser (Petrov et al., 2006), which performs surprisingly well without specialised NER features. We experiment with flattening the NNE annotations into linear NER variants with stacked categories, and explore the ability of a maximum entropy and a CRF NER system to reproduce them. The CRF performs substantially better, but is infeasible to train on the enormous stacked category sets. The flattened output of the Berkeley parser are almost competitive with the CRF. Our results demonstrate that the NNE corpus is feasible for statistical models to reproduce. We invite researchers to explore new, richer models of (joint) parsing and NER on this complex and challenging task. Our nested named entity corpus will improve a wide range of NLP tasks, such as coreference resolution and question answering, allowing automated systems to understand and exploit the true structure of named entities

Online learning of latent linguistic structure with approximate search

Automatic analysis of natural language data is a frequently occurring application of machine learning systems. These analyses often revolve around some linguistic structure, for instance a syntactic analysis of a sentence by means of a tree. Machine learning models that carry out structured prediction, as opposed to simpler machine learning tasks such as classification or regression, have therefore received considerable attention in the language processing literature. As an additional twist, the sought linguistic structures are sometimes not directly modeled themselves. Rather, prediction takes place in a different space where the same linguistic structure can be represented in more than one way. However, in a standard supervised learning setting, these prediction structures are not available in the training data, but only the linguistic structure. Since multiple prediction structures may correspond to the same linguistic structure, it is thus unclear which prediction structure to use for learning. One option is to treat the prediction structure as latent and let the machine learning algorithm guide this selection. In this dissertation we present an abstract framework for structured prediction. This framework supports latent structures and is agnostic of the particular language processing task. It defines a set of hyperparameters and task-specific functions which a user must implement in order to apply it to a new task. The advantage of this modularization is that it permits comparisons and reuse across tasks in a common framework. The framework we devise is based on the structured perceptron for learning. The perceptron is an online learning algorithm which considers one training instance at a time, makes a prediction, and carries out an update if the prediction was wrong. We couple the structured perceptron with beam search, which is a general purpose search algorithm. Beam search is, however, only approximate, meaning that there is no guarantee that it will find the optimal structure in a large search space. Therefore special attention is required to handle search errors during training. This has led to the development of special update methods such as early and max-violation updates. The contributions of this dissertation sit at the intersection of machine learning and natural language processing. With regard to language processing, we consider three tasks: Coreference resolution, dependency parsing, and joint sentence segmentation and dependency parsing. For coreference resolution, we start from an existing latent tree model and extend it to accommodate non-local features drawn from a greater structural context. This requires us to sacrifice exact for approximate search, but we show that, assuming sufficiently advanced update methods are used for the structured perceptron, then the richer scope of features yields a stronger coreference model. We take a transition-based approach to dependency parsing, where dependency trees are constructed incrementally by transition system. Latent structures for transition-based parsing have previously not received enough attention, partly because the characterization of the prediction space is non-trivial. We provide a thorough analysis of this space with regard to the ArcStandard with Swap transition system. This characterization enables us to evaluate the role of latent structures in transition-based dependency parsing. Empirically we find that the utility of latent structures depend on the choice of approximate search -- for greedy search they improve performance, whereas with beam search they are on par, or sometimes slightly ahead of, previous approaches. We then go on to extend this transition system to do joint sentence segmentation and dependency parsing. We develop a transition system capable of handling this task and evaluate it on noisy, non-edited texts. With a set of carefully selected baselines and data sets we employ this system to measure the effectiveness of syntactic information for sentence segmentation. We show that, in the absence of obvious orthographic clues such as punctuation and capitalization, syntactic information can be used to improve sentence segmentation. With regard to machine learning, our contributions of course include the framework itself. The task-specific evaluations, however, allow us to probe the learning machinery along certain boundary points and draw more general conclusions. A recurring observation is that some of the standard update methods for the structured perceptron with approximate search -- e.g., early and max-violation updates -- are inadequate when the predicted structure reaches a certain size. We show that the primary problem with these updates is that they may discard training data and that this effect increases as the structure size increases. This problem can be handled by using more advanced update methods that commit to using all the available training data. Here, we propose a new update method, DLaSO, which consistently outperforms all other update methods we compare to. Moreover, while this problem potentially could be handled by an increased beam size, we also show that this cannot fully compensate for the structure size and that the more advanced methods indeed are required.Bei der automatisierten Analyse natürlicher Sprache werden in der Regel maschinelle Lernverfahren eingesetzt, um verschiedenste linguistische Information wie beispielsweise syntaktische Strukturen vorherzusagen. Structured Prediction (dt. etwa Strukturvorhersage), also der Zweig des maschinellen Lernens, der sich mit der Vorhersage komplexer Strukturen wie formalen Bäumen oder Graphen beschäftigt, hat deshalb erhebliche Beachtung in der Forschung zur automatischen Sprachverarbeitung gefunden. In manchen Fällen ist es vorteilhaft, die gesuchte linguistische Struktur nicht direkt zu modellieren und stattdessen interne Repräsentationen zu lernen, aus denen dann die gewünschte linguistische Information abgeleitet werden kann. Da die internen Repräsentationen allerdings selten direkt in Trainingsdaten verfügbar sind, sondern erst aus der linguistischen Annotation inferiert werden müssen, kann es vorkommen, dass dabei mehrere äquivalente Strukturen in Frage kommen. Anstatt nun vor dem Lernen eine Struktur beliebig auszuwählen, kann man diese Entscheidung dem Lernverfahren selbst überlassen, welches dann selbständig die für das Modell am besten passende auszuwählen lernt. Unter diesen Umständen bezeichnet man die interne, nicht a priori bekannte Repräsentation für eine gesuchte Zielstruktur als latent. Diese Dissertation stellt ein Structured Prediction Framework vor, mit dem man den Vorteil latenter Repräsentationen nutzen kann und welches gleichzeitig von konkreten Anwendungsfällen abstrahiert. Diese Modularisierung ermöglicht die Wiederverwendbarkeit und den Vergleich über mehrere Aufgaben und Aufgabenklassen hinweg. Um das Framework auf ein reales Problem anzuwenden, müssen nur einige Hyperparameter definiert und einige problemspezifische Funktionen implementiert werden. Das vorgestellte Framework basiert auf dem Structured Perceptron. Der Perceptron-Algorithmus ist ein inkrementelles Lernverfahren (eng. online learning), bei dem während des Trainings einzelne Trainingsinstanzen nacheinander betrachtet werden. In jedem Schritt wird mit dem aktuellen Modell eine Vorhersage gemacht. Stimmt die Vorhersage nicht mit dem vorgegebenen Ergebnis überein, wird das Modell durch ein entsprechendes Update angepasst und mit der nächsten Trainingsinstanz fortgefahren. Der Structured Perceptron wird im vorgestellten Framework mit Beam Search kombiniert. Beam Search ist ein approximatives Suchverfahren, welches auch in sehr großen Suchräumen effizientes Suchen erlaubt. Es kann aus diesem Grund aber keine Garantie dafür bieten, dass das gefundene Ergebnis auch das optimale ist. Das Training eines Perceptrons mit Beam Search erfordert deshalb besondere Update-Methoden, z.B. Early- oder Max-Violation-Updates, um mögliche Vorhersagefehler, die auf den Suchalgorithmus zurückgehen, auszugleichen. Diese Dissertation ist an der Schnittstelle zwischen maschinellem Lernen und maschineller Sprachverarbeitung angesiedelt. Im Bereich Sprachverarbeitung beschäftigt sie sich mit drei Aufgaben: Koreferenzresolution, Dependenzparsing und Dependenzparsing mit gleichzeitiger Satzsegmentierung. Das vorgestellte Modell zur Koreferenzresolution ist eine Erweiterung eines existierenden Modells, welches Koreferenz mit Hilfe latenter Baumstrukturen repräsentiert. Dieses Modell wird um Features erweitert, mit denen nicht-lokale Abhängigkeiten innerhalb eines größeren strukturellen Kontexts modelliert werden. Die Modellierung nicht-lokaler Abhängigkeiten macht durch die kombinatorische Explosion der Features die Verwendung eines approximativen Suchverfahrens notwendig. Es zeigt sich aber, dass das so entstandene Koreferenzmodell trotz der approximativen Suche dem Modell ohne nicht-lokale Features überlegen ist, sofern hinreichend gute Update-Verfahren beim Lernen verwendet werden. Für das Dependenzparsing verwenden wir ein transitionsbasiertes Verfahren, bei dem Dependenzbäume inkrementell durch Transitionen zwischen definierten Zuständen konstruiert werden. Im ersten Schritt erarbeiten wir eine umfassende Analyse des latenten Strukturraums eines bekannten Transitionssystems, nämlich ArcStandard mit Swap. Diese Analyse erlaubt es uns, die Rolle der latenten Strukturen in einem transitionsbasierten Dependenzparser zu evaluieren. Wir zeigen dann empirisch, dass die Nützlichkeit latenter Strukturen von der Wahl des Suchverfahrens abhängt -- in Kombination mit Greedy-Search verbessern sich die Ergebnisse, in Kombination mit Beam-Search bleiben sie gleich oder verbessern sich leicht gegenüber vergleichbaren Modellen. Für die dritte Aufgabe wird der Parser noch einmal erweitert: wir entwickeln das Transitionssystem so weiter, dass es neben syntaktischer Struktur auch Satzgrenzen vorhersagt und testen das System auf verrauschten und unredigierten Textdaten. Mit Hilfe sorgfältig ausgewählter Baselinemodelle und Testdaten messen wir den Einfluss syntaktischer Information auf die Vorhersagequalität von Satzgrenzen und zeigen, dass sich in Abwesenheit orthographischer Information wie Interpunktion und Groß- und Kleinschreibung das Ergebnis durch syntaktische Information verbessert. Zu den wissenschaftlichen Beiträgen der Arbeit gehört einerseits das Framework selbst. Unsere problemspezifischen Experimente ermöglichen es uns darüber hinaus, die Lernverfahren zu untersuchen und allgemeinere Schlußfolgerungen zu ziehen. So finden wir z.B. in mehreren Experimenten, dass die etablierten Update-Methoden, also Early- oder Max-Violation-Update, nicht mehr gut funktionieren, sobald die vorhergesagte Struktur eine gewisse Größe überschreitet. Es zeigt sich, dass das Hauptproblem dieser Methoden das Auslassen von Trainingsdaten ist, und dass sie desto mehr Daten auslassen, je größer die vorhergesagte Struktur wird. Dieses Problem kann durch bessere Update-Methoden vermieden werden, bei denen stets alle Trainingsdaten verwendet werden. Wir stellen eine neue Methode vor, DLaSO, und zeigen, dass diese Methode konsequent bessere Ergebnisse liefert als alle Vergleichsmethoden. Überdies zeigen wir, dass eine erhöhte Beamgröße beim Suchen das Problem der ausgelassenen Trainingsdaten nicht kompensieren kann und daher keine Alternative zu besseren Update-Methoden darstellt

Learning meaning representations for text generation with deep generative models

This thesis explores conditioning a language generation model with auxiliary variables. By doing so, we hope to be able to better control the output of the language generator. We explore several kinds of auxiliary variables in this thesis, from unstructured continuous, to discrete, to structured discrete auxiliary variables, and evaluate their advantages and disadvantages. We consider three primary axes of variation: how interpretable the auxiliary variables are, how much control they provide over the generated text, and whether the variables can be induced from unlabelled data. The latter consideration is particularly interesting: if we can show that induced latent variables correspond to the semantics of the generated utterance, then by manipulating the variables, we have fine-grained control over the meaning of the generated utterance, thereby learning simple meaning representations for text generation. We investigate three language generation tasks: open domain conversational response generation, sentence generation from a semantic topic, and generating surface form realisations of meaning representations. We use a different type of auxiliary variable for each task, describe the reasons for choosing that type of variable, and critically discuss how much the task benefited from an auxiliary variable decomposition. All of the models that we use combine a high-level graphical model with a neural language model text generator. The graphical model lets us specify the structure of the text generating process, while the neural text generator can learn how to generate fluent text from a large corpus of examples. We aim to show the utility of such \textit{deep generative models} of text for text generation in the following work

Syntax und Valenz: Zur Modellierung kohärenter und elliptischer Strukturen mit Baumadjunktionsgrammatiken

Diese Arbeit untersucht das Verhältnis zwischen Syntaxmodell und lexikalischen Valenzeigenschaften anhand der Familie der Baumadjunktionsgrammatiken (TAG) und anhand der Phänomenbereiche Kohärenz und Ellipse. Wie die meisten prominenten Syntaxmodelle betreibt TAG eine Amalgamierung von Syntax und Valenz, die oft zu Realisierungsidealisierungen führt. Es wird jedoch gezeigt, dass TAG dabei gewisse Realisierungsidealisierungen vermeidet und Diskontinuität bei Kohärenz direkt repräsentieren kann; dass TAG trotzdem und trotz der im Vergleich zu GB, LFG und HPSG wesentlich eingeschränkten Ausdrucksstärke zu einer linguistisch sinnvollen Analyse kohärenter Konstruktionen herangezogen werden kann; dass der TAG-Ableitungsbaum für die indirekte Gapping-Modellierung eine ausreichend informative Bezugsgröße darstellt. Für  die direkte Repräsentation von Gapping-Strukturen wird schließlich ein baumbasiertes Syntaxmodell, STUG, vorgeschlagen, in dem Syntax und Valenz getrennt, aber verlinkt sind.    German law requires we state the prices in Germany for this publication. The hardcover price is 35.00 EUR; the softcover price is 25.00 EUR