Generating Semantic Graph Corpora with Graph Expansion Grammar

We introduce Lovelace, a tool for creating corpora of semantic graphs. The system uses graph expansion grammar as a representational language, thus allowing users to craft a grammar that describes a corpus with desired properties. When given such grammar as input, the system generates a set of output graphs that are well-formed according to the grammar, i.e., a graph bank. The generation process can be controlled via a number of configurable parameters that allow the user to, for example, specify a range of desired output graph sizes. Central use cases are the creation of synthetic data to augment existing corpora, and as a pedagogical tool for teaching formal language theory.Comment: In Proceedings NCMA 2023, arXiv:2309.0733

Methods for taking semantic graphs apart and putting them back together again

The thesis develops a competitive compositional semantic parser for Abstract Meaning Representation (AMR). This approach combines a neural model with mechanisms that echo ideas from compositional semantic construction in a new, simple dependency structure. The thesis first tackles the task of generating structured training data necessary for a compositional approach, by developing the linguistically motivated AM algebra. Encoding the terms over the AM algebra as dependency trees yields a simple semantic parsing model where neural tagging and dependency models predict interpretable, meaningful operations that construct the AMR.Diese Dissertation entwickelt einen kompositionellen semantischen Parser für den Graphformalismus Abstract Meaning Representation (AMR). Der Ansatz kombiniert ein neuronales Modell mit Mechanismen, die Ideen der klassischen kompositionellen semantischen Konstruktion widerspiegeln. Die Arbeit geht zunächst das Problem an, strukturierte latente Trainingsdaten zu erzeugen die für den kompositionellen Ansatz nötig sind. Für diesen Zweck wird die linguistisch motivierte AM Algebra entwickelt. Indem die Terme der AM Algebra als Dependenzbäume ausgedrückt werden, erhalten wir ein Modell für semantisches Parsen, in dem neuronale Tagging- und Dependenzmodelle interpretierbare, aussagekräftige Operationen vorhersagen die dann den AMR Graphen erzeugen. Damit erreicht das Modell starke Evaluationsergebnisse und deutliche Verbesserungen gegenüber einem weniger strukturierten Vergleichsmodell.DF

Learning words and syntactic cues in highly ambiguous contexts

The cross-situational word learning paradigm argues that word meanings can be approximated by word-object associations, computed from co-occurrence statistics between words and entities in the world. Lexicon acquisition involves simultaneously guessing (1) which objects are being talked about (the ”meaning”) and (2) which words relate to those objects. However, most modeling work focuses on acquiring meanings for isolated words, largely neglecting relationships between words or physical entities, which can play an important role in learning. Semantic parsing, on the other hand, aims to learn a mapping between entire utterances and compositional meaning representations where such relations are central. The focus is the mapping between meaning and words, while utterance meanings are treated as observed quantities. Here, we extend the joint inference problem of word learning to account for compositional meanings by incorporating a semantic parsing model for relating utterances to non-linguistic context. Integrating semantic parsing and word learning permits us to explore the impact of word-word and concept-concept relations. The result is a joint-inference problem inherited from the word learning setting where we must simultaneously learn utterance-level and individual word meanings, only now we also contend with the many possible relationships between concepts in the meaning and words in the sentence. To simplify design, we factorize the model into separate modules, one for each of the world, the meaning, and the words, and merge them into a single synchronous grammar for joint inference. There are three main contributions. First, we introduce a novel word learning model and accompanying semantic parser. Second, we produce a corpus which allows us to demonstrate the importance of structure in word learning. Finally, we also present a number of technical innovations required for implementing such a model

Exact Recursive Probabilistic Programming

Recursive calls over recursive data are widely useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful, but unfortunately, existing probabilistic programming languages do not perform exact inference on recursive calls over recursive data, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wide variety of recursion can be expressed naturally, and inference carried out exactly. For instance, probabilistic pushdown automata and their generalizations are easy to express, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types using program transformations related to defunctionalization and refunctionalization. These transformations are assured correct by a linear type system, and a successful choice of transformations, if there is one, is guaranteed to be found by a greedy algorithm