Grammar induction for mildly context sensitive languages using variational Bayesian inference

The following technical report presents a formal approach to probabilistic minimalist grammar induction. We describe a formalization of a minimalist grammar. Based on this grammar, we define a generative model for minimalist derivations. We then present a generalized algorithm for the application of variational Bayesian inference to lexicalized mildly context sensitive language grammars which in this paper is applied to the previously defined minimalist grammar

Montague Grammar Induction

We propose a computational model for inducing full-fledged combinatory categorial grammars from behavioral data. This model contrasts with prior computational models of selection in representing syntactic and semantic types as structured (rather than atomic) objects, enabling direct interpretation of the modeling results relative to standard formal frameworks. We investigate the grammar our model induces when fit to a lexicon-scale acceptability judgment dataset – Mega Acceptability – focusing in particular on the types our model assigns to clausal complements and the predicates that select them

Machine Learning and the Cognitive Basis of Natural Language

Articl

Inducing grammars from linguistic universals and realistic amounts of supervision

The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance can be achieved with plentiful supervised data. However, such resources are extremely costly to produce, making them an unlikely option for building NLP tools in under-resourced languages or domains. This dissertation is concerned with reducing the annotation required to learn NLP models, with the goal of opening up the range of domains and languages to which NLP technologies may be applied. In this work, we explore the possibility of learning from a degree of supervision that is at or close to the amount that could reasonably be collected from annotators for a particular domain or language that currently has none. We show that just a small amount of annotation input — even that which can be collected in just a few hours — can provide enormous advantages if we have learning algorithms that can appropriately exploit it. This work presents new algorithms, models, and approaches designed to learn grammatical information from weak supervision. In particular, we look at ways of intersecting a variety of different forms of supervision in complementary ways, thus lowering the overall annotation burden. Sources of information include tag dictionaries, morphological analyzers, constituent bracketings, and partial tree annotations, as well as unannotated corpora. For example, we present algorithms that are able to combine faster-to-obtain type-level annotation with unannotated text to remove the need for slower-to-obtain token-level annotation. Much of this dissertation describes work on Combinatory Categorial Grammar (CCG), a grammatical formalism notable for its use of structured, logic-backed categories that describe how each word and constituent fits into the overall syntax of the sentence. This work shows how linguistic universals intrinsic to the CCG formalism itself can be encoded as Bayesian priors to improve learning.Computer Science

A study of the use of natural language processing for conversational agents

Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional