11 research outputs found
Eighty Challenges Facing Speech Input/Output Technologies
ABSTRACT During the past three decades, we have witnessed remarkable progress in the development of speech input/output technologies. Despite these successes, we are far from reaching human capabilities of recognizing nearly perfectly the speech spoken by many speakers, under varying acoustic environments, with essentially unrestricted vocabulary. Synthetic speech still sounds stilted and robot-like, lacking in real personality and emotion. There are many challenges that will remain unmet unless we can advance our fundamental understanding of human communication -how speech is produced and perceived, utilizing our innate linguistic competence. This paper outlines some of these challenges, ranging from signal presentation and lexical access to language understanding and multimodal integration, and speculates on how these challenges could be met
Advanced techniques for personalized, interactive question answering
Using a computer to answer questions has been a human dream since the beginning of
the digital era. A first step towards the achievement of such an ambitious goal is to deal
with naturallangilage to enable the computer to understand what its user asks.
The discipline that studies the conD:ection between natural language and the represen~
tation of its meaning via computational models is computational linguistics. According
to such discipline, Question Answering can be defined as the task that, given a question
formulated in natural language, aims at finding one or more concise answers in the form
of sentences or phrases.
Question Answering can be interpreted as a sub-discipline of information retrieval
with the added challenge of applying sophisticated techniques to identify the complex
syntactic and semantic relationships present in text. Although it is widely accepted that
Question Answering represents a step beyond standard infomiation retrieval, allowing a
more sophisticated and satisfactory response to the user's information needs, it still shares
a series of unsolved issues with the latter.
First, in most state-of-the-art Question Answering systems, the results are created
independently of the questioner's characteristics, goals and needs. This is a serious limitation
in several cases: for instance, a primary school child and a History student may
need different answers to the questlon: When did, the Middle Ages begin?
Moreover, users often issue queries not as standalone but in the context of a wider
information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces
would encourage and accommodate the submission of multiple related questions
and handle the user's requests for clarification, interactive Question Answering is still at
its early stages:
Furthermore, an i~sue which still remains open in current Question Answering is
that of efficiently answering complex questions, such as those invoking definitions and
descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess
the correctness of answers to such complex questions.
.. These are the central research problems addressed by this thesis, and are solved as
follows.
An in-depth study on complex Question Answering led to the development of classifiers
for complex answers. These exploit a variety of lexical, syntactic and shallow
semantic features to perform textual classification using tree-~ernel functions for Support
Vector Machines.
The issue of personalization is solved by the integration of a User Modelling corn':
ponent within the the Question Answering model. The User Model is able to filter and
fe-rank results based on the user's reading level and interests.
The issue ofinteractivity is approached by the development of a dialogue model and a
dialogue manager suitable for open-domain interactive Question Answering. The utility
of such model is corroborated by the integration of an interactive interface to allow reference
resolution and follow-up conversation into the core Question Answerin,g system and
by its evaluation.
Finally, the models of personalized and interactive Question Answering are integrated
in a comprehensive framework forming a unified model for future Question Answering
research
Improvement on belief network framework for natural language understanding.
Mok, Oi Yan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 94-99).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis Goals --- p.3Chapter 1.3 --- Thesis Outline --- p.4Chapter 2 --- Background --- p.5Chapter 2.1 --- Natural Language Understanding --- p.5Chapter 2.1.1 --- Rule-based Approaches --- p.7Chapter 2.1.2 --- Phrase-spotting Approaches --- p.8Chapter 2.1.3 --- Stochastic Approaches --- p.9Chapter 2.2 --- Belief Network Framework - the N Binary Formulation --- p.11Chapter 2.2.1 --- Introduction of Belief Network --- p.11Chapter 2.2.2 --- The N Binary Formulation --- p.13Chapter 2.2.3 --- Semantic Tagging --- p.13Chapter 2.2.4 --- Belief Networks Development --- p.14Chapter 2.2.5 --- Goal Inference --- p.15Chapter 2.2.6 --- Potential Problems --- p.16Chapter 2.3 --- The ATIS Domain --- p.17Chapter 2.4 --- Chapter Summary --- p.19Chapter 3 --- Belief Network Framework - the One N-ary Formulation --- p.21Chapter 3.1 --- The One N-ary Formulation --- p.22Chapter 3.2 --- Belief Network Development --- p.23Chapter 3.3 --- Goal Inference --- p.24Chapter 3.3.1 --- Multiple Selection Strategy --- p.25Chapter 3.3.2 --- Maximum Selection Strategy --- p.26Chapter 3.4 --- Advantages of the One N-ary Formulation --- p.27Chapter 3.5 --- Chapter Summary --- p.29Chapter 4 --- Evaluation on the N Binary and the One N-ary Formula- tions --- p.30Chapter 4.1 --- Evaluation Metrics --- p.31Chapter 4.1.1 --- Accuracy Measure --- p.32Chapter 4.1.2 --- Macro-Averaging --- p.32Chapter 4.1.3 --- Micro-Averaging --- p.35Chapter 4.2 --- Experiments --- p.35Chapter 4.2.1 --- Network Dimensions --- p.38Chapter 4.2.2 --- Thresholds --- p.39Chapter 4.2.3 --- Overall Goal Identification --- p.43Chapter 4.2.4 --- Out-Of-Domain Rejection --- p.65Chapter 4.2.5 --- Multiple Goal Identification --- p.67Chapter 4.2.6 --- Computation --- p.68Chapter 4.3 --- Chapter Summary --- p.70Chapter 5 --- Portability to Chinese --- p.72Chapter 5.1 --- The Chinese ATIS Domain --- p.72Chapter 5.1.1 --- Word Tokenization and Parsing --- p.73Chapter 5.2 --- Experiments --- p.74Chapter 5.2.1 --- Network Dimension --- p.76Chapter 5.2.2 --- Overall Goal Identification --- p.77Chapter 5.2.3 --- Out-Of-Domain Rejection --- p.83Chapter 5.2.4 --- Multiple Goal Identification --- p.86Chapter 5.3 --- Chapter Summary --- p.88Chapter 6 --- Conclusions --- p.39Chapter 6.1 --- Summary --- p.89Chapter 6.2 --- Contributions --- p.91Chapter 6.3 --- Future Work --- p.92Bibliography --- p.94Chapter A --- The Communicative Goals --- p.100Chapter B --- Distribution of the Communicative Goals --- p.101Chapter C --- The Hand-Designed Grammar Rules --- p.103Chapter D --- The Selected Concepts for each Belief Network --- p.115Chapter E --- The Recalls and Precisions of the Goal Identifiers in Macro- Averaging --- p.12
The significance of silence. Long gaps attenuate the preference for ‘yes’ responses in conversation.
In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay – conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load ‘yes’ and ‘no’ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ‘no’ evokes an N400-effect relative to a fast ‘yes’, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive – but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ‘no’ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ‘no’ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response