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- A major scientific effort is dedicated to natural language understanding, which aims to be
- able to comprehend text, reason about it, and act upon it in an intelligent way. While
- specific use-cases or benchmarks can be solved with relatively simple systems, which either
- ignore word order ("bag-of-words" models) or treat it as a simple linear structure (such as
- the popular sequence-to-sequence framework allowing neural networks to learn tasks in an
- end-to-end fashion), understanding human language in general requires a hierarchical
- representation of meaning. Constructing this representation from text has been the goal of
- an extensive line of work in semantic parsing. While many semantic representation schemes
- have been proposed, they share many of their basic distinctions, such as between predicates
- (relations, states and events) and arguments (participants). This thesis focuses on a
- particular semantic representation scheme called Universal Conceptual Cognitive Annotation
- (UCCA), whose main design principles are support for all major linguistic semantic
- phenomena, cross-linguistic applicability, stability across translations, ease of annotation
- (even by those who are not experts in linguistics), and a modular architecture supporting
- multiple layers of semantic annotation. A fully automatic parser is presented, and evaluated
- on multiple languages (English, French and German). The parser, titled "TUPA"
- (transition-based UCCA parser), is able to learn very general graph structures: directed
- acyclic graphs over token sequences with non-terminal nodes for complex units, where these
- may cover discontinuous terminal yields. This general class of graphs covers the structures
- annotated in UCCA, as well as other representation schemes. TUPA is implemented as a
- transition-based parser, whose transition system supports these structural properties. Its
- transition classifier is a neural network equipped with a bidirectional long short-term
- memory (BiLSTM) module for calculating feature representations for the input. In an
- extensive comparison to conversion-based methods, as well as other classifier
- implementations, TUPA is shown to outperform all baselines in the task of UCCA parsing in
- both in-domain and out-of-domain settings in three languages. The parser is subsequently
- applied to two other semantic representation schemes, DM and AMR, and to syntactic
- dependencies in the Universal Dependencies (UD) scheme. This demonstrates that the flexible
- parser is usable not just for UCCA parsing. Furthermore, training TUPA in a multitask
- setting on all of these schemes improves its UCCA parsing accuracy, by effectively learning
- generalizations across the different representations: a shared model is thus able to apply
- semantic distinctions in one task, which have been learned for another. Finally, in an
- empirical comparison of the content of semantic and syntactic representations, we discover
- several aspects of divergence, i.e., differences in the content captured by these schemes.
- These have profound impact on the potential contribution of syntax to semantic parsing, and
- on the usefulness of each of the approaches for semantic tasks in natural language
- processing. I see semantic parsing as a means for computers to learn language. While
- different representations focus on different distinctions and do so with formally different
- structures, they share an overall goal, which is to support natural language processing
- applications, such as classifying text into categories, tagging it for linguistic
- properties, performing inference and reasoning, and generating new text according to some
- constraints (e.g., machine translation). The combined datasets annotated in every
- representation are an invaluable resource, which, used effectively, can greatly boost our
- achievements in language understanding and processing.
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