Intentions, implicatures and processing of complex questions (original) (raw)

The Process of Question Answering. Research Report No. 88

1977

The cOmputationAl model of question answering proposed by a .lamputer program,,QUALM, is a theory of conceptual information processing based 'bon models of, human memory organization. It has been developed from the perspective of' natural language processing in conjunction with story understanding systems. The p,ocesses in QUALM are divided into four phases: (1) conceptual categorization; (2) inferential analysis; (3) content specification; and (4) 'retrieval heuristict. QUALM provide a concrete criterion for judging the strengths and weaknesses'of store representations. As a theoretical model, QUALM is intended to describ general question answerinlg, where question antiering is viewed as a erbal communicb.tion device betieen people. (Author/KP) .

An Introduction to the Special Issue on Question Processing

Logic and Logical Philosophy

Logic of questions starts with a simple observation that all problem solving begins with a problem, which can be expressed in a question. This is a branch of logical inquiry which investigates the phenomena of posing, processing and answering questions in strict, formal and logical terms. Logic of questions (or erotetic logic, from Greek erotema-question) aims at solving three fundamental problems concerning questions and questioning. The first one is the problem of representation: how to formalize questions [Harrah, 2002]? Should they be considered independent linguistic entities, and formalized accordingly, as claimed by proponents of non-reductionist approach? Or should they be interpreted in terms of some other expressions, like imperatives, or demands for information, and represented via existing logics, as claimed by followers of various reductionist approaches? The second is the problem of semantics: what semantic properties should be ascribed to questions [Ciardelli et al., 2015; Ginzburg, 2012; Wiśniewski, 2015]? In particular, are they true or false, or bear some semantic characteristics other than truth values? The third is the problem of formalizing reasoning with questions. A question, before it is asked or posed, needs to be arrived at. What are the principles underlying this process [Hintikka et al., 2002; Wiśniewski, 1995]? What counts as an answer to a question, and what counts as a satisfactory one? What rules govern erotetic transformations, by which one question logically follows from the others, or from some declarative sentences?

Toward question-asking machines: the logic of questions and the inquiry calculus

2005

For over a century, the study of logic has focused on the algebra of logical statements. This work, first performed by George Boole, has led to the development of modern computers, and was shown by Richard T. Cox to be the foundation of Bayesian inference. Meanwhile the logic of questions has been much neglected. For our computing machines to be truly intelligent, they need to be able to ask relevant questions. In this paper I will show how the Boolean lattice of logical statements gives rise to the free distributive lattice of questions thus defining their algebra. Furthermore, there exists a quantity analogous to probability, called relevance, which quantifies the degree to which one question answers another. I will show that relevance is not only a natural generalization of information theory, but also forms its foundation.

A compositional view of questions

2019

We present a research on compositional treatment of questions in neo-Davidsonian event semantics style. (Champollion, 2011) presented a dynamic neo-Davidsonian compositional treatment of declarative sentences. Starting from complex formal examples we enrich Champollion's framework with ways of handling phenomena specific to questions-answers pair representation. This research can be applied in multiple fields ranging from questions answering tasks in information retrieval and chatbot programming to human interaction studies.

How much should you ask? On the question structure in QA systems

Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2018

Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner. However, users are still used to query-like systems where they type in keywords to search for answer. In this study we validate which parts of questions are essential for obtaining valid answer. In order to conclude that, we take advantage of LIME-a framework that explains prediction by local approximation. We find that grammar and natural language is disregarded by QA. Stateof-the-art model can answer properly even if 'asked' only with a few words with high coefficients calculated with LIME. According to our knowledge, it is the first time that QA model is being explained by LIME.