Computational Models of Miscommunication Phenomena

Miscommunication phenomena such as repair in dialogue are important indicators of the quality of communication. Automatic detection is therefore a key step toward tools that can characterize communication quality and thus help in applications from call center management to mental health monitoring. However, most existing computational linguistic approaches to these phenomena are unsuitable for general use in this way, and particularly for analyzing human–human dialogue: Although models of other-repair are common in human-computer dialogue systems, they tend to focus on specific phenomena (e.g., repair initiation by systems), missing the range of repair and repair initiation forms used by humans; and while self-repair models for speech recognition and understanding are advanced, they tend to focus on removal of “disfluent” material important for full understanding of the discourse contribution, and/or rely on domain-specific knowledge. We explain the requirements for more satisfactory models, including incrementality of processing and robustness to sparsity. We then describe models for self- and other-repair detection that meet these requirements (for the former, an adaptation of an existing repair model; for the latter, an adaptation of standard techniques) and investigate how they perform on datasets from a range of dialogue genres and domains, with promising results.EPSRC. Grant Number: EP/10383/1; Future and Emerging Technologies (FET). Grant Number: 611733; German Research Foundation (DFG). Grant Number: SCHL 845/5-1; Swedish Research Council (VR). Grant Numbers: 2016-0116, 2014-3

Re-Representing Metaphor: Modelling metaphor perception using dynamically contextual distributional semantics

In this paper, we present a novel context-dependent approach to modelling word meaning, and apply it to the modelling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualised representations by dynamically projecting low-dimensional subspaces; in these \textit{ad hoc} spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesise that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualising work inherent in the re-representational process

Detecting Summary-worthy Sentences: the Effect of Discourse Features

We examine the benefit of a variety of discourse and semantic features for the identification of summary-worthy content in narrative stories. Using logistic regression models, we find that the most informative features are those that relate to the narrative structure of a text. We show that automatic methods for feature extraction perform significantly worse than full manual annotation, but that with optimization, a fully automatic approach can outperform a variety of existing extractive approaches to summarization

Probabilistic Record Type Lattices for Incremental Reference Processing

This chapter addresses the issue of incorporating probabilistic type-theoretic inference into an incremental dialogue framework, using processing of referring expressions (that is, their interpretation and generation) as a test case. Within our framework, we model reference processing in a psycholinguistically plausible way– that is, in a strictly left-to-right, word-by-word, incremental fashion. We additionally show how the model is capable of processing disfluent referring expressions while making use of the information the disfluency conveys to reflect psycholinguistic results on the effect on processing speed. Our model incorporates probabilistic Type Theory with Records (Cooper et al., 2014) and order-theoretic models of probability and information theory (Knuth, 2005) into a formal dialogue system that reflects the psycholinguistic evidence. In Section 2 we introduce the challenge of modelling reference processing, and overview some previous approaches. In Section 3 we describe the semantic and dialogue framework we use and how we enrich it with probabilistic record type lattices. In Section 4 we describe how our model can simulate psycholinguistic results in reference processing and we finish with a discussion and conclusion