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Intentions And Information In Discourse
Full text linkThis paper is about the flow of inference between communicative intentions, discourse structure and the domain during discourse processing. We augment a theory of discourse interpretation with a theory of distinct mental attitudes and reasoning about them, in order to provide an account of how the attitudes interact with reasoning about discourse structure. INTRODUCTION The flow of inference between communicative intentions and domain information is often essential to discourse processing. It is well reflected in this discourse from Moore and Pollack (1992): (1)a. George Bush supports big business. b. He's sure to veto House Bill 1711. There are at least three different interpretations. Consider Context 1: in this context the interpreter I believes that the author A wants to convince him that (1b) is true. For example, the context is one in which I has already uttered Bush won't veto any more bills. I reasons that A's linguistic behavior was intentional, and therefore that A believ..
Interactive task learning from corrective feedback
Full text linkIn complex teaching scenarios it can be difficult for teachers to exhaustively express all information a learner requires to master a task. However, the teacher, who will have internalised the task's objectives, will be able to identify good and bad actions in specific scenarios and would be able to formulate advice upon observing those scenarios. This thesis focuses on the design, implementation and evaluation of models that enable experts to teach agents through such situated feedback in an Interactive Task Learning (ITL) setting.
There is a class of highly natural speech acts which have so far gone largely unexplored in the domain of ITL: how to exploit evidence provided by a teacher when they correct the learning agent by articulating the mistake they just made. The aim of this thesis is to show that such speech acts can be exploited in an ITL to learn a task in a data efficient manner. Further we aim to show that this is made possible by capturing within the learning agent's models the constraints that are imposed by dialogue coherence. A dialogue is coherent if the current utterance relates to a salient part of its dialogue context with a specific coherence relation, such as explanation, contrast, correction, or elaboration. Our model will exploit the semantics of these relations to restrict the set of possible interpretations of the teacher's utterance and how the utterance relates to the objects involved in the action the teacher is giving feedback on.
We test our hypothesis on a tower building task where the set of allowed towers is constrained by rules. The agent starts out ignorant of these rules, and perhaps more fundamentally, is also unaware of the domain-level concepts used to define the rules and natural language terms that denote those concepts. We develop an agent which utilises the coherence of the extended dialogue to interpret and disambiguate the teacher's feedback, and utilises this (estimated) interpretation to refine its model of the domain, the mapping from NL descriptions to their denotations, given their observable visual features, and the planning problem being addressed. We extend this model to deal with utterances containing anaphora and to deal with an imperfect teacher: that is, one who occasionally doesn't provide the correct correction in a timely way, and/or who is confident, but wrong, about the learner's ability to identify from her utterance the salient part of the context that it is intended to correct. Finally, we use these ideas to learn the manner in which actions should be performed
The Automatic Acquisition of Knowledge about Discourse Connectives
Full text linkInstitute for Communicating and Collaborative SystemsThis thesis considers the automatic acquisition of knowledge about discourse connectives.
It focuses in particular on their semantic properties, and on the relationships that hold between
them. There is a considerable body of theoretical and empirical work on discourse connectives.
For example, Knott (1996) motivates a taxonomy of discourse connectives based on
relationships between them, such as HYPONYMY and EXCLUSIVE, which are defined in terms
of substitution tests. Such work requires either great theoretical insight or manual analysis of
large quantities of data. As a result, to date no manual classification of English discourse connectives
has achieved complete coverage. For example, Knott gives relationships between only
about 18% of pairs obtained from a list of 350 discourse connectives.
This thesis explores the possibility of classifying discourse connectives automatically, based
on their distributions in texts. This thesis demonstrates that state-of-the-art techniques in lexical
acquisition can successfully be applied to acquiring information about discourse connectives.
Central to this thesis is the hypothesis that distributional similarity correlates positively with
semantic similarity. Support for this hypothesis has previously been found for word classes
such as nouns and verbs (Miller and Charles, 1991; Resnik and Diab, 2000, for example), but
there has been little exploration of the degree to which it also holds for discourse connectives.
We investigate the hypothesis through a number of machine learning experiments. These
experiments all use unsupervised learning techniques, in the sense that they do not require any
manually annotated data, although they do make use of an automatic parser. First, we show
that a range of semantic properties of discourse connectives, such as polarity and veridicality
(whether or not the semantics of a connective involves some underlying negation, and whether
the connective implies the truth of its arguments, respectively), can be acquired automatically
with a high degree of accuracy. Second, we consider the tasks of predicting the similarity
and substitutability of pairs of discourse connectives. To assist in this, we introduce a novel
information theoretic function based on variance that, in combination with distributional similarity,
is useful for learning such relationships. Third, we attempt to automatically construct
taxonomies of discourse connectives capturing substitutability relationships. We introduce a
probability model of taxonomies, and show that this can improve accuracy on learning substitutability
relationships. Finally, we develop an algorithm for automatically constructing or
extending such taxonomies which uses beam search to help find the optimal taxonomy
Using shallow parsing to improve robustness of hand-crafted grammars
No full textThe goal of this project is to use supervised learning to train a model which will be able
to create logical forms, relying purely on shallow methods. For the supervised portion,
we used annotated data from the Redwoods Treebank as the source of the gold-standard
semantic representations. We then used the raw text of each sentence as input into
our shallow processing component and used the output to create our underspecified
semantic representations.
We used the fully specified semantic representations to train a maximum entropy model
which then predicted which elements should be added to the underspecified representation.
This involved the creation of maximum entropy models for each of the conditions
in question, and then collating them to create (more) fully specified representations
from our underspecified ones.
The new representations were evaluated against the gold-standard Redwoods representations,
showing that the general principles here do provide reasonable results, though
there is much room for improvement and future work
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Processing embodied conversation for interactive task learning
No full textLifelong learning is a long-standing goal of human-robot interaction. One approach to achieving this is through Interactive Task Learning (ITL) scenarios, in which the learner uses natural interaction with a user, acting as a teacher, to learn new tasks. One type of natural interaction is embodied conversation, initiated by an instruction such as “move the one red cube in front of a blue cylinder”. A key challenge for ITL is that the learner’s domain conceptualization may entirely lack the concepts necessary for solving the task. In other words, the teacher’s natural language expressions are neologisms to the learner (“red” or “cube”, say, in our example) that may denote concepts that are not a part of the learner’s conceptualization of the domain at all. To handle such un-foreseen possibilities, the learner must perform interactive symbol grounding: that is, they must expand their hypothesis space of possible states to include newly discovered concepts and learn in real-time how to recognize objects denoted by this unforeseen concept to successfully solve the task, using the teacher’s messages as evidence.
This thesis studies three ways in which the formal semantic analysis of embodied conversation can aid ITL. Firstly, we study how processing referential expressions like “the one red cube” with their logical consequences (e.g. that there is exactly one red cube in the environment) aids interactive symbol grounding. Secondly, we look into designing dialogue strategies under unawareness by quantifying the value of asking questions which require the teacher’s effort vs. risking solving tasks with current beliefs, which will be costly if wrong. Our unique contribution is that our learning models cope with an ever-expanding hypothesis space of possible states and actions that arise in ITL. Finally, we consider corrections of the agent’s execution actions, which arise when the ITL agent attempts to solve the task but performs a sub-optimal action in the environment (e.g., picking a red cylinder rather than a red cube), exposing its false beliefs and so prompting the teacher to express the source of the error (e.g., “No, this is a cylinder”). Such corrective feedback triggers belief revision, which, by exploiting the semantic consequences of the fact that it’s a correction (in this case, that the picked object is not a cube), complements and further improves the learning process.
We study these three facets by developing and evaluating neuro-symbolic methods for interactive symbol grounding and policy learning. Through our experiments, we conclude that agents who use formal semantic analysis when processing embodied conversation, outperform learners lacking these capabilities
Lexically Specified Derivational Control in Combinatory Categorial Grammar
No full textInstitute for Communicating and Collaborative SystemsThis dissertation elaborates several refinements to the Combinatory Categorial Grammar (CCG) framework which are motivated by phenomena in parametrically diverse languages such as English, Dutch, Tagalog, Toba Batak and Turkish. I present Multi-Modal Combinatory Categorial Grammar, a formulation of CCG which incorporates devices and category constructors from related categorical frameworks and demonstrate the effectiveness of these modifications both for providing parsimonious linguistic analyses and for improving the representation of the lexicon and computational processing.
Altogether, this dissertation provides many formal, linguistic, and computational justifications for the central thesis that this dissertation puts forth- that an explanatory theory of natural language grammar can be based on a categorial grammar formalism which allows cross-linguistic variation only in the lexicon and has computationally attractive properties
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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