Syntactic inductive biases for deep learning methods

Le débat entre connexionnisme et symbolisme est l'une des forces majeures qui animent le développement de l'Intelligence Artificielle. L'apprentissage profond et la linguistique théorique sont les domaines d'études les plus représentatifs pour les deux écoles respectivement. Alors que la méthode d'apprentissage profond a fait des percées impressionnantes et est devenue la principale raison de la récente prospérité de l'IA pour l'industrie et les universités, la linguistique et le symbolisme occupent quelque domaines importantes, notamment l'interprétabilité et la fiabilité. Dans cette thèse, nous essayons de construire une connexion entre les deux écoles en introduisant des biais inductifs linguistiques pour les modèles d'apprentissage profond. Nous proposons deux familles de biais inductifs, une pour la structure de circonscription et une autre pour la structure de dépendance. Le biais inductif de circonscription encourage les modèles d'apprentissage profond à utiliser différentes unités (ou neurones) pour traiter séparément les informations à long terme et à court terme. Cette séparation fournit un moyen pour les modèles d'apprentissage profond de construire les représentations hiérarchiques latentes à partir d'entrées séquentielles, dont une représentation de niveau supérieur est composée et peut être décomposée en une série de représentations de niveau inférieur. Par exemple, sans connaître la structure de vérité fondamentale, notre modèle proposé apprend à traiter l'expression logique en composant des représentations de variables et d'opérateurs en représentations d'expressions selon sa structure syntaxique. D'autre part, le biais inductif de dépendance encourage les modèles à trouver les relations latentes entre les mots dans la séquence d'entrée. Pour le langage naturel, les relations latentes sont généralement modélisées sous la forme d'un graphe de dépendance orienté, où un mot a exactement un nœud parent et zéro ou plusieurs nœuds enfants. Après avoir appliqué cette contrainte à un modèle de type transformateur, nous constatons que le modèle est capable d'induire des graphes orientés proches des annotations d'experts humains, et qu'il surpasse également le modèle de transformateur standard sur différentes tâches. Nous pensons que ces résultats expérimentaux démontrent une alternative intéressante pour le développement futur de modèles d'apprentissage profond.The debate between connectionism and symbolism is one of the major forces that drive the development of Artificial Intelligence. Deep Learning and theoretical linguistics are the most representative fields of study for the two schools respectively. While the deep learning method has made impressive breakthroughs and became the major reason behind the recent AI prosperity for industry and academia, linguistics and symbolism still holding some important grounds including reasoning, interpretability and reliability. In this thesis, we try to build a connection between the two schools by introducing syntactic inductive biases for deep learning models. We propose two families of inductive biases, one for constituency structure and another one for dependency structure. The constituency inductive bias encourages deep learning models to use different units (or neurons) to separately process long-term and short-term information. This separation provides a way for deep learning models to build the latent hierarchical representations from sequential inputs, that a higher-level representation is composed of and can be decomposed into a series of lower-level representations. For example, without knowing the ground-truth structure, our proposed model learns to process logical expression through composing representations of variables and operators into representations of expressions according to its syntactic structure. On the other hand, the dependency inductive bias encourages models to find the latent relations between entities in the input sequence. For natural language, the latent relations are usually modeled as a directed dependency graph, where a word has exactly one parent node and zero or several children nodes. After applying this constraint to a transformer-like model, we find the model is capable of inducing directed graphs that are close to human expert annotations, and it also outperforms the standard transformer model on different tasks. We believe that these experimental results demonstrate an interesting alternative for the future development of deep learning models

Knowledge-enhanced neural grammar Induction

Natural language is usually presented as a word sequence, but the inherent structure of language is not necessarily sequential. Automatic grammar induction for natural language is a long-standing research topic in the field of computational linguistics and still remains an open problem today. From the perspective of cognitive science, the goal of a grammar induction system is to mimic children: learning a grammar that can generalize to infinitely many utterances by only consuming finite data. With regard to computational linguistics, an automatic grammar induction system could be beneficial for a wide variety of natural language processing (NLP) applications: providing syntactic analysis explicitly for a pipeline or a joint learning system; injecting structural bias implicitly into an end-to-end model. Typically, approaches to grammar induction only have access to raw text. Due to the huge search space of trees as well as data sparsity and ambiguity issues, grammar induction is a difficult problem. Thanks to the rapid development of neural networks and their capacity of over-parameterization and continuous representation learning, neural models have been recently introduced to grammar induction. Given its large capacity, introducing external knowledge into a neural system is an effective approach in practice, especially for an unsupervised problem. This thesis explores how to incorporate external knowledge into neural grammar induction models. We develop several approaches to combine different types of knowledge with neural grammar induction models on two grammar formalisms — constituency and dependency grammar. We first investigate how to inject symbolic knowledge, universal linguistic rules, into unsupervised dependency parsing. In contrast to previous state-of-the-art models that utilize time-consuming global inference, we propose a neural transition-based parser using variational inference. Our parser is able to employ rich features and supports inference in linear time for both training and testing. The core component in our parser is posterior regularization, where the posterior distribution of the dependency trees is constrained by the universal linguistic rules. The resulting parser outperforms previous unsupervised transition-based dependency parsers and achieves performance comparable to global inference-based models. Our parser also substantially increases parsing speed over global inference-based models. Recently, tree structures have been considered as latent variables that are learned through downstream NLP tasks, such as language modeling and natural language inference. More specifically, auxiliary syntax-aware components are embedded into the neural networks and are trained end-to-end on the downstream tasks. However, such latent tree models either struggle to produce linguistically plausible tree structures, or require an external biased parser to obtain good parsing performance. In the second part of this thesis, we focus on constituency structure and propose to use imitation learning to couple two heterogeneous latent tree models: we transfer the knowledge learned from a continuous latent tree model trained using language modeling to a discrete one, and further fine-tune the discrete model using a natural language inference objective. Through this two-stage training scheme, the discrete latent tree model achieves stateof-the-art unsupervised parsing performance. The transformer is a newly proposed neural model for NLP. Transformer-based pre-trained language models (PLMs) like BERT have achieved remarkable success on various NLP tasks by training on an enormous corpus using word prediction tasks. Recent studies show that PLMs can learn considerable syntactical knowledge in a syntaxagnostic manner. In the third part of this thesis, we leverage PLMs as a source of external knowledge. We propose a parameter-free approach to select syntax-sensitive self-attention heads from PLMs and perform chart-based unsupervised constituency parsing. In contrast to previous approaches, our head-selection approach only relies on raw text without any annotated development data. Experimental results on both English and eight other languages show that our approach achieves competitive performance

Unsupervised structure induction and multimodal grounding

Structured representations build upon symbolic abstraction (e.g., words in natural language and visual concepts in natural images), offer a principled way of encoding our perceptions about the physical world, and enable the human-like generalization of machine learning systems. The predominant paradigm for learning structured representations of the observed data has been supervised learning, but it is limited in several respects. First, supervised learning is challenging given the scarcity of labeled data. Second, conventional approaches to structured prediction have been relying on a single modality (e.g., either images or text), ignoring the learning cues that may have been specified in and can be readily obtained from other modalities of data. In this thesis, we investigate unsupervised approaches to structure induction in a multimodal setting. Unsupervised learning is inherently difficult in general, let alone inducing complex and discrete structures from data without direct supervision. By considering the multimodal setting, we leverage the alignments between different data modalities (e.g., text, audio, and images) to facilitate the learning of structure-induction models, e.g., knowing that the individual words in ``a white pigeon'' always appear with the same visual object, a language parser is likely to treat them as a whole (i.e., phrase). The multimodal learning setting is practically viable because multimodal alignments are generally abundant. For example, they can be found in online posts such as news and tweets that usually contain images and associated text, and in (YouTube) videos, where audio, scripts, and scenes are synchronized and grounded in each other. We develop structure-induction models, which are capable of exploiting bimodal image-text alignments, for two modalities: (1) for natural language, we consider unsupervised syntactic parsing with phrase-structure grammars and regularize the parser by using visual image groundings; and (2) for visual images, we induce scene graph representations by mapping arguments and predicates in the text to their visual counterparts (i.e., visual objects and relations among them) in an unsupervised manner. While useful, crossmodal alignments are not always abundantly available on the web, e.g., the alignments between non-speech audio and text. We tackle the challenge by sharing the visual modality between image-text alignment and image-audio alignment; images function as a pivot and connect audio and text. The contributions of this thesis span from model development to data collection. We demonstrated the feasibility of applying multimodal learning techniques to unsupervised structure induction and multimodal alignment collection. Our work opens up new avenues for multimodal and unsupervised structured representation learning

Large Language Models

Artificial intelligence is making spectacular progress, and one of the best examples is the development of large language models (LLMs) such as OpenAI's GPT series. In these lectures, written for readers with a background in mathematics or physics, we give a brief history and survey of the state of the art, and describe the underlying transformer architecture in detail. We then explore some current ideas on how LLMs work and how models trained to predict the next word in a text are able to perform other tasks displaying intelligence.Comment: 46 page

Large Language Models as General Pattern Machines

We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstraction and Reasoning Corpus (ARC), a general AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern completion proficiency can be partially retained even when the sequences are expressed using tokens randomly sampled from the vocabulary. These results suggest that without any additional training, LLMs can serve as general sequence modelers, driven by in-context learning. In this work, we investigate how these zero-shot capabilities may be applied to problems in robotics -- from extrapolating sequences of numbers that represent states over time to complete simple motions, to least-to-most prompting of reward-conditioned trajectories that can discover and represent closed-loop policies (e.g., a stabilizing controller for CartPole). While difficult to deploy today for real systems due to latency, context size limitations, and compute costs, the approach of using LLMs to drive low-level control may provide an exciting glimpse into how the patterns among words could be transferred to actions.Comment: 21 pages, 25 figures. To appear at Conference on Robot Learning (CoRL) 202

Multilingual Neural Translation

Machine translation (MT) refers to the technology that can automatically translate contents in one language into other languages. Being an important research area in the field of natural language processing, machine translation has typically been considered one of most challenging yet exciting problems. Thanks to research progress in the data-driven statistical machine translation (SMT), MT is recently capable of providing adequate translation services in many language directions and it has been widely deployed in various practical applications and scenarios. Nevertheless, there exist several drawbacks in the SMT framework. The major drawbacks of SMT lie in its dependency in separate components, its simple modeling approach, and the ignorance of global context in the translation process. Those inherent drawbacks prevent the over-tuned SMT models to gain any noticeable improvements over its horizon. Furthermore, SMT is unable to formulate a multilingual approach in which more than two languages are involved. The typical workaround is to develop multiple pair-wise SMT systems and connect them in a complex bundle to perform multilingual translation. Those limitations have called out for innovative approaches to address them effectively. On the other hand, it is noticeable how research on artificial neural networks has progressed rapidly since the beginning of the last decade, thanks to the improvement in computation, i.e faster hardware. Among other machine learning approaches, neural networks are known to be able to capture complex dependencies and learn latent representations. Naturally, it is tempting to apply neural networks in machine translation. First attempts revolve around replacing SMT sub-components by the neural counterparts. Later attempts are more revolutionary by fundamentally changing the whole core of SMT with neural networks, which is now popularly known as neural machine translation (NMT). NMT is an end-to-end system which directly estimate the translation model between the source and target sentences. Furthermore, it is later discovered to capture the inherent hierarchical structure of natural language. This is the key property of NMT that enables a new training paradigm and a less complex approach for multilingual machine translation using neural models. This thesis plays an important role in the evolutional course of machine translation by contributing to the transition of using neural components in SMT to the completely end-to-end NMT and most importantly being the first of the pioneers in building a neural multilingual translation system. First, we proposed an advanced neural-based component: the neural network discriminative word lexicon, which provides a global coverage for the source sentence during the translation process. We aim to alleviate the problems of phrase-based SMT models that are caused by the way how phrase-pair likelihoods are estimated. Such models are unable to gather information from beyond the phrase boundaries. In contrast, our discriminative word lexicon facilitates both the local and global contexts of the source sentences and models the translation using deep neural architectures. Our model has improved the translation quality greatly when being applied in different translation tasks. Moreover, our proposed model has motivated the development of end-to-end NMT architectures later, where both of the source and target sentences are represented with deep neural networks. The second and also the most significant contribution of this thesis is the idea of extending an NMT system to a multilingual neural translation framework without modifying its architecture. Based on the ability of deep neural networks to modeling complex relationships and structures, we utilize NMT to learn and share the cross-lingual information to benefit all translation directions. In order to achieve that purpose, we present two steps: first in incorporating language information into training corpora so that the NMT learns a common semantic space across languages and then force the NMT to translate into the desired target languages. The compelling aspect of the approach compared to other multilingual methods, however, lies in the fact that our multilingual extension is conducted in the preprocessing phase, thus, no change needs to be done inside the NMT architecture. Our proposed method, a universal approach for multilingual MT, enables a seamless coupling with any NMT architecture, thus makes the multilingual expansion to the NMT systems effortlessly. Our experiments and the studies from others have successfully employed our approach with numerous different NMT architectures and show the universality of the approach. Our multilingual neural machine translation accommodates cross-lingual information in a learned common semantic space to improve altogether every translation direction. It is then effectively applied and evaluated in various scenarios. We develop a multilingual translation system that relies on both source and target data to boost up the quality of a single translation direction. Another system could be deployed as a multilingual translation system that only requires being trained once using a multilingual corpus but is able to translate between many languages simultaneously and the delivered quality is more favorable than many translation systems trained separately. Such a system able to learn from large corpora of well-resourced languages, such as English → German or English → French, has proved to enhance other translation direction of low-resourced language pairs like English → Lithuania or German → Romanian. Even more, we show that kind of approach can be applied to the extreme case of zero-resourced translation where no parallel data is available for training without the need of pivot techniques. The research topics of this thesis are not limited to broadening application scopes of our multilingual approach but we also focus on improving its efficiency in practice. Our multilingual models have been further improved to adequately address the multilingual systems whose number of languages is large. The proposed strategies demonstrate that they are effective at achieving better performance in multi-way translation scenarios with greatly reduced training time. Beyond academic evaluations, we could deploy the multilingual ideas in the lecture-themed spontaneous speech translation service (Lecture Translator) at KIT. Interestingly, a derivative product of our systems, the multilingual word embedding corpus available in a dozen of languages, can serve as a useful resource for cross-lingual applications such as cross-lingual document classification, information retrieval, textual entailment or question answering. Detailed analysis shows excellent performance with regard to semantic similarity metrics when using the embeddings on standard cross-lingual classification tasks

Automating the Formal Verification of Software

Formally verified correctness is one of the most desirable properties of software systems. Despite great progress made toward verification via interactive proof assistants, such as Coq and Isabelle/HOL, such verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs either through reasoning using precomputed facts or using machine learning to model proofs and then perform biased search through the proof space. However, models in existing tools fail to capture the richness present in proofs, such as the information the programmer has access to when writing proofs and the natural language contained within variable names. Furthermore, these prior models do not make use of variations in the learning process and advances in large language models. In this dissertation, I develop tools to improve proof synthesis and to enable fully automating more verification. I first present TacTok, a proof-synthesis tool that models proofs using both the partial proof written thus far and the semantics of the proof state. I then present Diva, a proof-synthesis tool that controls the learning process to produce a diverse set of models and, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof’s correctness), efficiently combines these models to improve the overall proving power. I then present Passport, a proof-synthesis tool that systematically explores different ways of encoding identifiers in proofs to improve synthesis. Finally, I present Baldur, a proof-synthesis tool that uses transformer-based pretrained large language models fine-tuned on proofs to generate and repair whole proofs at once, rather than one step at a time. This dissertation contributes new ideas for improving automated proof synthesis and empirically demonstrates that the improvement is significant on large benchmarks consisting of open-source software projects

On continuation-passing transformations and expected cost analysis

We define a continuation-passing style (CPS) translation for a typed \u3bb-calculus with probabilistic choice, unbounded recursion, and a tick operator - for modeling cost. The target language is a (non-probabilistic) \u3bb-calculus, enriched with a type of extended positive reals and a fixpoint operator. We then show that applying the CPS transform of an expression M to the continuation \u3bb v. 0 yields the expected cost of M. We also introduce a formal system for higher-order logic, called EHOL, prove it sound, and show it can derive tight upper bounds on the expected cost of classic examples, including Coupon Collector and Random Walk. Moreover, we relate our translation to Kaminski et al.'s ert-calculus, showing that the latter can be recovered by applying our CPS translation to (a generalization of) the classic embedding of imperative programs into \u3bb-calculus. Finally, we prove that the CPS transform of an expression can also be used to compute pre-expectations and to reason about almost sure termination

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques