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Found 18 papers, 1 papers with code
Discrete and Probabilistic Classifier-based Semantics
We present a formal semantics (a version of Type Theory with Records) which places classifiers of perceptual information at the core of semantics.
Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems
A Natural Language Understanding (NLU) component can be used in a dialogue system to perform intent classification, returning an N-best list of hypotheses with corresponding confidence estimates.
In Search of Meaning and Its Representations for Computational Linguistics
In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.
Semantic Classification and Learning Using a Linear Tranformation Model in a Probabilistic Type Theory with Records
Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with this account and provide an alternative account of classification learning that addresses the observed problems.
Bayesian Classification and Inference in a Probabilistic Type Theory with Records
We propose a probabilistic account of semantic inference and classification formulated in terms of probabilistic type theory with records, building on Cooper et.
Towards Negotiative Dialogue for the Talkamatic Dialogue Manager
The paper describes a number of dialogue phenomena associated with negotiative dialogue, as implemented in a development version of the Talkamatic Dialogue Manager (TDM).
Topic and genre in dialogue
In this paper we argue that topic plays a fundamental role in conversations, and that the concept is needed in addition to that of genre to define interactions.
We went to look for meaning and all we got were these lousy representations: aspects of meaning representation for computational semantics
In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.
Semantic shift in social networks
Just as the meaning of words is tied to the communities in which they are used, so too is semantic change.
Distribution is not enough: going Firther
Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space.
User-initiated Sub-dialogues in State-of-the-art Dialogue Systems
We test state of the art dialogue systems for their behaviour in response to user-initiated sub-dialogues, i. e. interactions where a system question is responded to with a question or request from the user, who thus initiates a sub-dialogue.
