Data Mining Practical Machine Learning Tools and Techniques

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From the Publisher:
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Traditional mechanistic accounts of language processing derive almost entirely from the study of monologue. Yet, the most natural and basic form of language use is dialogue. As a result, these accounts may only offer limited theories of the mechanisms that un- derlie language processing in general. We propose a mechanistic account of dialogue, the interactive alignment account, and use it to de- rive a number of predictions about basic language processes. The account assumes that, in dialogue, the linguistic representations em- ployed by the interlocutors become aligned at many levels, as a result of a largely automatic process. This process greatly simplifies production and comprehension in dialogue. After considering the evidence for the interactive alignment model, we concentrate on three aspects of processing that follow from it. It makes use of a simple interactive inference mechanism, enables the development of local di- alogue routines that greatly simplify language processing, and explains the origins of self-monitoring in production. We consider the need for a grammatical framework that is designed to deal with language in dialogue rather than monologue, and discuss a range of implica- tions of the account.

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Toward a mechanistic psychology of dialogue

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., I don’t know) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as the objective function in neural models. Experimental results demonstrate that the proposed MMI models produce more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets and in human evaluations.

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A Diversity-Promoting Objective Function for Neural Conversation Models

Video summarization is a challenging problem in part because knowing which part of a video is important requires prior knowledge about its main topic. We present TVSum, an unsupervised video summarization framework that uses title-based image search results to find visually important shots. We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic. However, because titles are free-formed, unconstrained, and often written ambiguously, images searched using the title can contain noise (images irrelevant to video content) and variance (images of different topics). To deal with this challenge, we developed a novel co-archetypal analysis technique that learns canonical visual concepts shared between video and images, but not in either alone, by finding a joint-factorial representation of two data sets. We introduce a new benchmark dataset, TVSum50, that contains 50 videos and their shot-level importance scores annotated via crowdsourcing. Experimental results on two datasets, SumMe and TVSum50, suggest our approach produces superior quality summaries compared to several recently proposed approaches.

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TVSum: Summarizing web videos using titles

The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester.

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Toward Conversational Human-Computer Interaction

In this paper, we describe an architecture for conversational systems that enables human-like performance along several important dimensions. First, interpretation is incremental, multi-level, and involves both general and task- and domain-specific knowledge. Second, generation is also incremental, proceeds in parallel with interpretation, and accounts for phenomena such as turn-taking, grounding and interruptions. Finally, the overall behavior of the system in the task at hand is determined by the (incremental) results of interpretation, the persistent goals and obligations of the system, and exogenous events of which it becomes aware. As a practical matter, the architecture supports a separation of responsibilities that enhances portability to new tasks and domains.

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An architecture for more realistic conversational systems

CommandTalk (Moore et al., 1997) is a spokenlanguage interface to the ModSAF battlefield simulator that allows simulation operators to generate and execute military exercises by creating forces and control measures, assigning missions to forces, and controlling the display (Ceranowicz, 1994). CommandTalk consists of independent, cooperating agents interacting through SRI's Open Agent Architecture (OAA) (Martin et al., 1998). This architecture allows components to be developed independently, and then flexibly and dynamically combined to support distributed computation. Most of the agents that compose CommandTalk have been described elsewhere !for more detail, see (Moore et al., 1997)). This paper describes extensions to CommandTalk to support spoken dialogue. While we make no theoretical claims about the nature and structure of dialogue, we are influenced by the theoretical work of (Grosz and Sidner, 1986) and will use terminology from that tradition when appropriate. We also follow (Chu-Carroll and Brown, 1997) in distinguishing task initiative and dialogue initiative. Section 2 demonstrates the dialogue capabilities of CommandTalk by way of an extended example. Section 3 describes how language in CommandTalk is modeled for understanding and generation. Section 4 describes the architecture of the dialogue manager in detail. Section 5 compares CommandTalk with other spo-

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The CommandTalk Spoken Dialogue System

Mobile interfaces need to allow the user and system to adapt their choice of communication modes according to user preferences, the task at hand, and the physical and social environment. We describe a multimodal application architecture which combines finite-state multimodal language processing, a speech-act based multimodal dialogue manager, dynamic multimodal output generation, and user-tailored text planning to enable rapid prototyping of multimodal interfaces with flexible input and adaptive output. Our testbed application MATCH (Multimodal Access To City Help) provides a mobile multimodal speech-pen interface to restaurant and sub-way information for New York City.

Amanda Stent

Papers

TVSum: Summarizing web videos using titles

Toward Conversational Human-Computer Interaction

An architecture for more realistic conversational systems

The CommandTalk Spoken Dialogue System

MATCH: An Architecture for Multimodal Dialogue Systems