Proceedings of the 13 ESSLLI Student Session - Multiple Choices ...

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Proceedings of the 13 th ESSLLI Student Session measure strategy 1 strategy 2 strategy 3 gap 14.0 % 351 ms 18.7 % 358 ms 17.4 % 362 ms speaker a 31.4 % 1259 ms 35.9 % 1009 ms 36.5 % 1079 ms speaker b 39.3 % 1415 ms 39.8 % 1165 ms 40.8 % 1225 ms clash 15.4 % 1184 ms 5.6 % 317 ms 5.3 % 278 ms Table 3: Distribution and mean duration of dialogue states for three turn-taking strategies with pseudo-speech. measure strategy 1 strategy 2 strategy 3 gap 14.1 % 528 ms 20.7 % 477 ms 18.9 % 454 ms speaker a 36.2 % 1764 ms 40.5 % 1456 ms 34.7 % 1232 ms speaker b 26.2 % 1437 ms 24.8 % 1307 ms 42.0 % 1540 ms clash 23.5 % 1915 ms 4.0 % 253 ms 4.4 % 243 ms Table 4: Distribution and mean duration of dialogue states for three turn-taking strategies with KCoRS speakers. 51 Measuring Turn-Management Success The dialogue state can be described by the current speech state of each of the dialogue participants, with each speech state being either talk or sil. For two-party dialogue, this results in four states: two “good” states where either one of the dialogue participants is talking and two “bad” states: Clashes when both participants talk simultaneously, and gaps with neither of them talking. According to Sacks et al. (1974), speakers try to optimize their behaviour so as to minimize the occurence of both clashes and gaps. That is why we choose clashes and gaps as basic measures for turn-taking success. Slight gaps and clashes occur all the time, but they are not always perceptually relevant. We thus decided to calculate the proportion of clashes and gaps over the course of the dialogue as well as their mean duration. For evaluation purposes, we set up two artificial dialogue participants and let them talk with each other for about 10 minutes for each of the following strategies. We recorded the internal states and calculated the described measures. The audio itself was recorded but not further analyzed in the evaluation. The results of the strategies described below are shown in tables tables 3 and 4. 52 Strategy 1: Talk When Nobody Talks Rule: Start an utterance when neither you nor your interlocutor is talking. (Implicitly: Continue talking until your utterance is finished.) The performance with this strategy strongly depends on the round-trip time from one agent’s decision to take the turn until the other agent notices the turn being taken. The shorter the lags introduced by the talking agent’s internal communication, audio transmission, prosodic processing and classification, and the listening agent’s internal communication, the more likely it is for a dialogue participant to notice its interlocutor talking (and then listen until he has finished) before she has started talking herself. For longer lags, the DP will decide to talk even though its interlocutor may already have started talking himself. As can be seen, this strategy leads to a large amount of clahes. 53 Strategy 2: Hush When Both Talk Rule as above, plus: Stop your utterance when both you and your interlocutor are talking. 22
Proceedings of the 13 th ESSLLI Student Session The rule proves effective in reducing simultaneous talk as clashes are reduced by 65 % (pseudo-speech) and over 80 % (KCors) respectively. At the same time, this strategy leads to the introduction of utterance truncations, when an utterance was stopped prematuerly. (Actually, the majority of utterances (71 % for pseudo-speech) was truncated, but many of these truncations occur in the silent phases before or after the actual talk and do not have any deteriorating effect on the perceived turn-taking performance.) Truncations could be reduced with a higher probability to hush during SoT. 54 Strategy 3: Start Talking Early The previous strategies only react after turns have started or ended. In order to initiate actions early and anticipates turn changes, this strategy exploits the EoT class of the speech analysis (which was ignored before) in the first rule: Start an utterance, when you are not talking and your interlocutor is ending their turn or has already finished. By starting utterance planning before the interlocutor’s preceding utterance is finished, the dialogue participant can hide some of the lag introduced by its speech generation module. The duration of both gaps and clashes is reduced compared to strategy 2, for gaps because turns will be taken over more quickly and for clashes due to the original talk-owner noticing the turn-change earlier, avoiding the start of a new utterance. The durations for gaps and clashes with this strategy are similar to those reported for parts of the Verbmobil corpus by Weilhammer and Rabold (2003), with 363 ms and 331 ms respectively. 3 Performance could be further improved by using a lower probability to hush during EoT. 6 Conclusion and Future Directions We have presented a flexible, modular architecture for dialogue strategy evaluation where arbitrary pairings of human users and artificial dialogue participants can be created. We have discussed a case-study in this environment, where pairs of artificial DPs converse in real time via audio. Each DP autonomously decides on their turn-taking behaviour (start or stop talking) based on a local analysis of the audio signal and using machine-learned classifiers. We tested these with corpora of simplified speech and achieve good recognition performance. Three implemented turn-management rulesets, all of them locallymanaged in the sense of Sacks et al. (1974), i. e. not requiring dialogue memory, were shown to create increasingly realistic behavioural patterns. We plan to use the components developed for this system in an interactive speech dialogue system. For the speech state classification, we will need normalized prosodic features that allow for speaker independent speech state classification. At the same time, ASR will make features relative to syllable information (stress patterns, speech rate, ...) accessible, as well as word hypotheses. We may also want to look into classifier confidence scores, only emitting speech state changes if the classifier is reasonably certain. In real dialogue, the problem of hesitations arises. Our classification will have to be extended to distinguish hesitational interruptions from normal EoT. We would also like to identify positions in a turn where a back-channelling utterance might be appropriate. 3 Note, that their numbers are for turn changes only, while we do not distinguish between gaps at turn changes and at turn continuations. 23
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Page 3 and 4: Contents Preface . . . . . . . . .
Page 5 and 6: Preface Proceedings of the 13 th ES
Page 9 and 10: mechanism of the encoded function.
Page 13 and 14: The table below presents experiment
Page 17 and 18: SIMULATING SPOKEN DIALOGUE WITH A F
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Page 31 and 32: We now state our update semantics f
Page 33 and 34: 3 Comparison With a Partition Seman
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Page 37 and 38: BARE PREDICATION AND KINDS ∗ Bert
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Page 43 and 44: 43 Non-bare predication and non-uni
Page 45 and 46: References Proceedings of the 13 th
Page 47 and 48: DIAGRAMMATIC REASONING WITH ENHANCE
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Page 57 and 58: FICTIONAL CONTINGENCIES Gemma Celes
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Proceedings of the 13 th ESSLLI Stu
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TOWARDS A NEW CHARACTERISATION OF C
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The following paper continues this
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only one increasing chain completel
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the minimal step width seems to be
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etrieved in order to check it again
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Results The percentages of correct
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The complexity of relative clauses
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References Proceedings of the 13 th
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A SALIENCE-DRIVEN APPROACH TO SPEEC
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2. the linguistic salience or “re
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where the expectations provided by
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A LOGIC WITH A CONDITIONAL PROBABIL
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• v : F orC × W −→ {0, 1} is
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Lemma 4 M ∗ is a measurable model
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is a tautology. Now we can equivale
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A PROOF-THEORETIC APPROACH TO FRENC
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References Abeillé, A., Godard, D.
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INFINITE GAMES FROM AN INTUITIONIST
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6 Further problems Proceedings of t
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pointers to the text, containing th
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example in a comparatively short te
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Such an example is: Constant modifi
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WORD SPACE MODELS OF SEMANTIC SIMIL
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tween semantic similarity and seman
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wu & palmer 0.0 0.2 0.4 0.6 0.8 1.0
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frequency F−score 0 100 200 300 4
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EXAMINING THE NOTICING FUNCTION OF
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effects of input flood and individu
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CDIPROVER3: A TOOL FOR PROVING DERI
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Example 8. Consider the TRS given i
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THE RANK(S) OF A TOTALLY LEXICALIST
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EXPRESSING CONJUNCTIVE AND AGGREGAT
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(Rule) (Semantic Action) Qwh → In
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(⇐) We will prove, by induction o
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1 Introduction INTERROGATION IN DYN
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to hold for questions to be felicit
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5 Further research One goal for a s
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THE SEMANTIC CHANGE OF THE FRENCH -
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6 Conclusion In this paper, I inves
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ADVERSARY IMPLICATURES ∗ Grégoir
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2.2.1 The derivation of Q-Implicatu
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of the first, the relation of Contr
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List of authors Martin Avanzini Mar
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