set of n-tuples description Vw(λ~x.φ) {w | Vw(λ~x.φ) = E} (E ⊆ Dn) question proposition partition answerhood {v | Vv(λ~x.φ) = Vw(λ~x.φ)} {{v | Vv(λ~x.φ) = Vw(λ~x.φ)} | w ∈ W} conditions Figure 3.4: Extension and intension of assertions, abstracts and questions (33) Does John love Mary? ?love(john,mary) extension at w: {v | Vv(love(john,mary)) = Vw(love(john,mary))} intension set: {{v | Vv(love(john,mary)) = Vw(love(john,mary))} | w ∈ W} The question relation of the question in example (34) is λx.love(john, x). The extension at w is the complete answer at w: the set of worlds that agree with w on the objects that satisfy the question relation. Each answer roughly corresponds to a subset E of the set of objects that are initially plausible answer candidates. The intension can again be derived by abstracting over w. Worlds that are indistinguishable with respect to their answers, are grouped together. Therefore a wh-question produces a partition in as many blocks, as there are subsets of the set of plausible answer candidates. (34) Whom does John love? ?x.love(john.x) extension at w: {v | Vv(λx.love(john, x)) = Vw(λx.love(john, x)) ) intension set: {{v | Vv(λx.love(john, x)) = Vw(λx.love(john, x))} | w ∈ W} Now we can motivate definition 8 and 9 of extension and intension for each type of expression in the language. The intuitions are summarised in figure 3.4. These notions can be expressed more elegantly in a two-sorted type-theory (Groenendijk and Stokhof 1989). For an assertion, the extension is a truth value that indicates whether the asserted is satisfied at the world of evaluation. The intension is a characteristic function: a function from worlds to truth values. In general, the intension of an expression is a function that maps worlds onto the extensions. Based on the intension, we can derive what one may call the intension set: the set of worlds that share a particular extension value. What value that is, depends on the success conditions of the particular way the expression is used. For an assertion, that value is typically the truth value ‘1’. For a term, the extension is its interpretation in a world: an object from the domain, as in definition 6. Its intension is a function from worlds to objects: an individual concept. We can again derive an intension set. A minimal requirement is that the term is defined in a particular world. So a natural intension set for a term would be the set of worlds for which the extension is defined. This corresponds to the presupposition of the term. An n-place abstract expresses some (question) relation. Its extension is analogous to the interpretation of a basic predicate symbol. It denotes the set of n-tuples of objects that satisfy the relation at a particular world. Again, we can define an intension set. Any set of applicable tuples E ⊆ Dn may count as the typical extension value of an n-place abstract. The set of worlds with non-empty answer sets corresponds to the conventional presupposition of the question corresponding to the n-place abstract. 3.3. UPDATE SEMANTICS WITH QUESTIONS 83 Finally, a question has as its extension in a world, the complete answer to the question at that world. Answers are propositions, modelled by a set of worlds. In this case, the set of worlds that agree on their extension of the n-place abstract. So the extension of a question equals the intension set of an abstract. The intension of an question is again the functin that maps worlds onto their extensions. The corresponding intension set is therefore a partition of the set of worlds where the question makes sense. Here is the definition of the extension of an expression relative to a particular context of evaluation: a model, a world and an assignment. Sequences of expressions φ;ψ are dealt with when we come to updates, in definition 15. Definition 8 (Extension) Given a model M, define for each φ ∈ L0 the extension at w under g Vw,g(φ) = { 1, if M,w, g |= φ 0, otherwise Vw,g(?~x.φ) = {v | Vv,g(λ~x.φ) = Vw,g(λ~x.φ)} where Vw,g(λ~x.φ) = {~ d ∈ Dn | M, g[~x 7→ ~ d],w |= φ} 2 Note that the definition for assertions is equal to that of abstracts for ~x = 〈〉 when you take {〈〉} as ‘1’ and ∅ as ‘0’. Also yes/no questions are an instance of the general case for questions. If the empty sequence 〈〉 makes the question relation true, this may be classified as a ‘yes’. If no sequence makes the question relation true, the answer was ‘no’. Proposition 1 (Yes/No Questions) For each model M, world w and assignment g Vw,g(?φ) = { {v | Vv,g(λ〈〉.φ) = Vw,g(λ〈〉.φ) = {〈〉}}, if M, g,w |= φ (’yes’) {v | Vv,g(λ〈〉.φ) = Vw,g(λ〈〉.φ) = ∅}, otherwise (’no’) Proof By definition. 2 The intension of an expression is the extension abstracted over the context. Given an assignment g, this produces for each φ a representative function 〈[φ]〉g from worlds to extensions. Definition 9 (Intension) Given model M and assignment g define for each φ ∈ L 〈[φ]〉g = λv.Vv,g(φ). 2 Based on the representative function we can derive an equivalence relation. Worlds are grouped according to their extensions. Using the earlier notation for equivalence under a given function we write '〈[φ]〉g . The notion of [[φ]]g, the set of worlds that satisfies a certain expression φ under g corresponds to what we called the intension set. It defines the content of an utterance. For assertives, [[φ]]g is called the proposition expressed by φ. For questions we can define a corresponding notion. [[?~x.φ]]g is the partition generated by '〈[?~x.φ]〉g . This partition structure is called the issue expressed by ?~x.φ. Definition 10 (Proposition; Issue) Given model M and assignment g, define for each φ ∈ L [[φ]]g = {w | Vw,g(φ) = 1} [[?~x.φ]]g = {{w ∈ W | Vw,g(?~x.φ) = Vv,g(?~x.φ)} | v ∈ W} 2 Incidently, this notion of content can be seen as a projection of the inverse function of the intension applied to the typical result of an utterance of that type, specified by the success 84 CHAPTER 3. INQUIRY conditions. The typical result of an assertion is the truth value 1; a denial has 0. The typical result of a question is a proposition corresponding to a so called nominal answer: a set of tuples of objects that possibly satisfy the question relation. A nominal answer E ⊆ Dn is called possible in case there is some world conceivable that has it as a complete answer. Impossible answers are those which are not supported by any world, for instance because they violate some meaning postulate or because some presuppositions in the question are not satisfied. Definition 11 (Projected Inverse) For any function f :: A → B define f−1 :: B → pow(A), the projected inverse of f , by f−1(b) = {a ∈ A | f (a) = b} (b ∈ B) 2 Proposition 2 (Inverse) For each φ ∈ L, its content is the projection of the inverse intension on a typical result: (i) [[φ]]g = 〈[φ]〉−1 g (1) (ii) [[?~x.φ]]g = { 〈[?~x.φ]〉−1 g {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } Proof (i) 〈[φ]〉−1 g (1) = {w | 〈[φ]〉g(w) = 1} (def) = {w | Vw,g(φ) = 1} (appl, def) = [[φ]]g (*) A nominal answer E ⊆ Dn to some question ?~x.φ is called possible, in case there is some v ∈ W such that Vv,g(λ~x.φ) = E. (ii) { 〈[?~x.φ]〉−1 g ({v | Vv(λ~x.φ) = E}) | E ⊆ Dn, E possible } = {{w | 〈[?~x.φ]〉g(w) = {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } (def) = {{w | Vw,g(?~x.φ) = {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } (appl, def) = {{w | Vw,g(?~x.φ) = {v | Vv(λ~x.φ) = E} = Vu,g(?~x.φ)} | u ∈ W} (def, *) = [[?~x.φ]]g 2 What to make of this result? For assertives, it highlights the special role played by the truth value ‘1’. For interrogatives, the function 〈[φ]〉−1 g applied to propositions that describe nominal answers is the identity function. This means that the two ways of generating a partition are essentially the same: grouping worlds that are indistinguishable with respect to an answer, or generating possible answers on the basis of a distribution of nominal answers (provided they are possible) and then finding the worlds to support them. So for the equivalence relation induced by a question, it does not matter whether you take indistinguishability with respect to the n-place abstract or with respect to the question: '〈[λx.φ]〉g = '〈[?x.φ]〉g. The distinction between extensions and intensions of questions is therefore less important for an update semantics with questions, where the equivalence relation is the central notion. 3.3.4 Information States Now we turn to a definition of the set of information states ΣW, on the basis of a given information space W. How are the separate data and structure aspects of an information state to be modelled? The factual information of an agent is modelled by a set of possible worlds: the worlds that are compatible with the information of the agent. This set is called the data set here. The semantic content of a question corresponds to a partition. This has 3.3. UPDATE SEMANTICS WITH QUESTIONS 85 been motivated above. We will use an equivalence relation to model the structure of issues. Partitions and equivalence relations are interchangeable. There is a choice. We can have the equivalence relation range over the complete information space, or over the particular data set of an agent. The choice only makes a difference from the external perspective. The first option makes most sense when issues are public: they are known to each participant on the basis of what was said earlier. Public issues may continue to play a role structuring the information space after they have been resolved for some particular agent. The second option makes most sense when it concerns private issues of an agent. Moreover, questions can contain referential expressions that are only defined in a certain subset of worlds. This context dependency is best modelled by relativising issues to a particular data set. Therefore we take the second option, to limit issues to a particular data set. The aspects of data and structure can now be combined into one equivalence relation σ over a subset of the set of all possible worlds W. From the equival

/pdf/dialogue-models-for-inquiry-and-transaction-3m4a1vf5dn.pdf

Dialogue Models for Inquiry and Transaction

This paper gives an overview of the Jacob project. This project involves the construction of a 3D virtual environment where an animated humanlike agent called Jacob gives instruction to the user. The project investigates virtual reality techniques and focuses on three issues: the software engineering aspects of building a virtual reality system, the integration of natural language interaction and other interaction modalities, and the use of agent technology. The Jacob agent complies with the H-Anim standard. It has been given a task model and an instruction model in order to teach the user a particular task. The results of the project can be generalised so that the agent can be used to instruct other tasks in other virtual environments.

/pdf/jacob-an-animated-instruction-agent-in-virtual-reality-4iljchp5x8.pdf

Jacob - An Animated Instruction Agent in Virtual Reality

We studied whether the gaze direction of users indicates whom they are speaking or listening to in multiparty conversations. Results show when someone is listening or speaking to individuals, there is indeed a high probability that the person looked at is the person listened (p=88%) or spoken to (p=77%). We implemented these findings in a multi-agent conversational system that uses eye input to gauge which agent the user is listening or speaking to.

/pdf/why-conversational-agents-should-catch-the-eye-3e2moko8qt.pdf

Why conversational agents should catch the eye

This chapter describes Conversational Agents (CAs) in the context of Systems Engineering. A CA is a computer program which interacts with a user through natural language dialogue and provides some form of service. CA technology has two points of interest to systems engineers: the use of systems engineering techniques in CA research and the application of CAs in project development. CAs offer the opportunity to automate more complex applications than are feasible with conventional web interfaces. Currently such applications require a human expert in the domain to mediate between the user and the application. The CA effectively replaces the human expert. This chapter reviews the current capabilities of various CA technologies, outlines a development methodology for systems engineering practitioners interested in developing real world applications and suggests a number of directions for systems engineers who wish to participate in CA research.

/pdf/systems-engineering-and-conversational-agents-1lpek7c0qf.pdf

Systems Engineering and Conversational Agents

Problem statement: Feature selection is a task of crucial importance for the application of
machine learning in various domains. In addition, the recent increase of data dimensionality poses a
severe challenge to many existing feature selection approaches with respect to efficiency and
effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself
directly to feature selection; however this direct application is hindered by the recent increase of data
dimensionality. Therefore adapting genetic algorithm to cope with the high dimensionality of the data
becomes increasingly appealing. Approach: In this study, we proposed an adapted version of genetic
algorithm that can be applied for feature selection in high dimensional data. The proposed approach is
based essentially on a variable length representation scheme and a set of modified and proposed
genetic operators. To assess the effectiveness of the proposed approach, we applied it for cues phrase
selection and compared its performance with a number of ranking approaches which are always
applied for this task. Results and Conclusion: The results provide experimental evidences on the
effectiveness of the proposed approach for feature selection in high dimensional data.

/pdf/feature-selection-for-high-dimensional-data-an-evolutionary-5f2esw8abq.pdf

Feature selection for high dimensional data: An evolutionary filter approach.

Cet article presente les activites actuelles et futures du projets SCHISMA. Les principaux points de recherche dans les activites en cours sont la collecte des donnees de dialogues, l'analyse des dialogues et la modelisation des dialogues. Les experiences utilisant des produits commerciaux en lanagage naturel sont etudiees. Et enfin, pour definir les futurs axes de recherche et de developpement concernant le langage, la contribution possible des reseaux neuronaux et de la logique floue dans le projet et l'acces aux autoroutes de l'information est examinee

SCHISMA: A Natural Language Accessible Theatre Information and Booking System

SCHISMA is a joint research project of KPN (Royal PTT Nederland) and the University of Twente. The
project aims at providing a natural language dialogue system which interfaces a database containing
information about theatre performances in a certain city or region. The interface should make it possible
to ask about performances in general, to tune in to a specific performance and, if desired, make a
reservation for this performance. Research until now has concentrated on various aspects of realising
such a theatre information and booking system. Among these aspects are the building of a Wizard of Oz
environment for the acquisition of a corpus of dialogues for this domain, analysis and tagging of the
dialogue corpus, recognition of domain-specific concepts (actors, authors, plays, dates, etc.), syntactic
analysis and dialogue modelling. The emphasis in this paper will be on the analysis of individual
utterances at various levels. Most important for the project are short and medium term goals of delivering
prototype systems that allow demonstration of the system and evaluation of the design choices. Due to
these goals the project does not strive at incorporating advanced but isolated research results on discourse
models and syntactic and semantic analysis. Rather we investigate how to identify user preferences and
how to embed systems like these in a more comprehensive environment of information and transaction
services.

Language analysis for dialogue management in a theatre information and booking system

Pragmatics in Language Technology: introduction

This paper gives an overview of activities in the SCHISMA project now and in the near and fur­ther future. Main points of discussion on current activities are collecting data on dialogues, dialogue analysis and dialogue modelling. Some attention is also paid to experiences using a the commercially available product Natural Lan­guage. In the discussion of future plans and develop­ments, topics such as speech, integration of speech and language, and the possible contribu­tion of neural networks and fuzzy logic in the project are considered.

Jan Schaake

Papers

SCHISMA: A Natural Language Accessible Theatre Information and Booking System

Language analysis for dialogue management in a theatre information and booking system

Pragmatics in Language Technology: introduction

SCHISMA: A Natural Language Accessible Theatre Information and Booking System