26,663 research outputs found
Learning Features that Predict Cue Usage
Our goal is to identify the features that predict the occurrence and
placement of discourse cues in tutorial explanations in order to aid in the
automatic generation of explanations. Previous attempts to devise rules for
text generation were based on intuition or small numbers of constructed
examples. We apply a machine learning program, C4.5, to induce decision trees
for cue occurrence and placement from a corpus of data coded for a variety of
features previously thought to affect cue usage. Our experiments enable us to
identify the features with most predictive power, and show that machine
learning can be used to induce decision trees useful for text generation.Comment: 10 pages, 2 Postscript figures, uses aclap.sty, psfig.te
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations
Dialog act (DA) recognition is a task that has been widely explored over the
years. Recently, most approaches to the task explored different DNN
architectures to combine the representations of the words in a segment and
generate a segment representation that provides cues for intention. In this
study, we explore means to generate more informative segment representations,
not only by exploring different network architectures, but also by considering
different token representations, not only at the word level, but also at the
character and functional levels. At the word level, in addition to the commonly
used uncontextualized embeddings, we explore the use of contextualized
representations, which provide information concerning word sense and segment
structure. Character-level tokenization is important to capture
intention-related morphological aspects that cannot be captured at the word
level. Finally, the functional level provides an abstraction from words, which
shifts the focus to the structure of the segment. We also explore approaches to
enrich the segment representation with context information from the history of
the dialog, both in terms of the classifications of the surrounding segments
and the turn-taking history. This kind of information has already been proved
important for the disambiguation of DAs in previous studies. Nevertheless, we
are able to capture additional information by considering a summary of the
dialog history and a wider turn-taking context. By combining the best
approaches at each step, we achieve results that surpass the previous
state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the
most widely explored corpora for the task. Furthermore, by considering both
past and future context, simulating annotation scenario, our approach achieves
a performance similar to that of a human annotator on SwDA and surpasses it on
MRDA.Comment: 38 pages, 7 figures, 9 tables, submitted to JAI
A Machine Learning Approach to the Classification of Dialogue Utterances
The purpose of this paper is to present a method for automatic classification
of dialogue utterances and the results of applying that method to a corpus.
Superficial features of a set of training utterances (which we will call cues)
are taken as the basis for finding relevant utterance classes and for
extracting rules for assigning these classes to new utterances. Each cue is
assumed to partially contribute to the communicative function of an utterance.
Instead of relying on subjective judgments for the tasks of finding classes and
rules, we opt for using machine learning techniques to guarantee objectivity.Comment: 12 pages, using nemlap.sty, harvard.sty and agsm.bst, to appear in
Proceedings of NeMLaP-2, Bilkent University, Ankara, Turke
Referential and visual cues to structural choice in visually situated sentence production
We investigated how conceptually informative (referent preview) and conceptually uninformative (pointer to referent’s location) visual cues affect structural choice during production of English transitive sentences. Cueing the Agent or the Patient prior to presenting the target-event reliably predicted the likelihood of selecting this referent as the sentential Subject, triggering, correspondingly, the choice between active and passive voice. Importantly, there was no difference in the magnitude of the general Cueing effect between the informative and uninformative cueing conditions, suggesting that attentionally driven structural selection relies on a direct automatic mapping mechanism from attentional focus to the Subject’s position in a sentence. This mechanism is, therefore, independent of accessing conceptual, and possibly lexical, information about the cued referent provided by referent preview
Limited Attention and Discourse Structure
This squib examines the role of limited attention in a theory of discourse
structure and proposes a model of attentional state that relates current
hierarchical theories of discourse structure to empirical evidence about human
discourse processing capabilities. First, I present examples that are not
predicted by Grosz and Sidner's stack model of attentional state. Then I
consider an alternative model of attentional state, the cache model, which
accounts for the examples, and which makes particular processing predictions.
Finally I suggest a number of ways that future research could distinguish the
predictions of the cache model and the stack model.Comment: 9 pages, uses twoside,cl,lingmacro
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Research on the structure of dialogue has been hampered for years because
large dialogue corpora have not been available. This has impacted the dialogue
research community's ability to develop better theories, as well as good off
the shelf tools for dialogue processing. Happily, an increasing amount of
information and opinion exchange occur in natural dialogue in online forums,
where people share their opinions about a vast range of topics. In particular
we are interested in rejection in dialogue, also called disagreement and
denial, where the size of available dialogue corpora, for the first time,
offers an opportunity to empirically test theoretical accounts of the
expression and inference of rejection in dialogue. In this paper, we test
whether topic-independent features motivated by theoretical predictions can be
used to recognize rejection in online forums in a topic independent way. Our
results show that our theoretically motivated features achieve 66% accuracy, an
improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent
Identification of Agreement and Disagreement in Social Media Dialogue},
author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference},
year={2013}
- …