Spectral learning with non negative probabilities for finite state automaton

Glaude, Hadrien; Enderli, Cyrille; Pietquin, Olivier

doi:10.1109/asru.2015.7404776

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…Future works will include more sophisticated ways to compute the transition matrices such as spectral methods [31]. It would also be interesting to introduce modifications of the algorithms to allow more general value function representations than linear parametrization.…”

Section: Resultsmentioning

confidence: 99%

A Stochastic Model for Computer-Aided Human-Human Dialogue

Barlier¹,

Laroche²,

Pietquin³

2016

Interspeech 2016

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In this paper we introduce a novel model for computer-aided human-human dialogue. In this context, the computer aims at improving the outcome of a human-human task-oriented dialogue by intervening during the course of the interaction. While dialogue state and topic tracking in human-human dialogue have already been studied, few work has been devoted to the sequential part of the problem, where the impact of the system's actions on the future of the conversation is taken into account. This paper addresses this issue by first modelling human-human dialogue as a Markov Reward Process. The task of purposely taking part into the conversation is then optimised within the Linearly Solvable Markov Decision Process framework. Utterances of the Conversational Agent are seen as perturbations in this process, which aim at satisfying the user's long-term goals while keeping the conversation natural. Finally, results obtained by simulation suggest that such an approach is suitable for computer-aided human-human dialogue and is a first step towards three-party dialogue.

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Section: Resultsmentioning

confidence: 99%

A Stochastic Model for Computer-Aided Human-Human Dialogue

Barlier¹,

Laroche²,

Pietquin³

2016

Interspeech 2016

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“…Intuitively this requires that the rows of M be "well scattered" in the probability simplex, but not to the extent of "separable". Separability-based HMM identification has been considered in Barlier et al [2015], Glaude et al [2015]. However, the way they construct second-order statistics is very different from ours.…”

Section: Identifiability Of Hmms From Pairwise Co-occurrence Probabil...mentioning

confidence: 95%

Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling

Huang,

Fu,

Sidiropoulos

2018

Preprint

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We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable for cases where the available sample size is large enough to accurately estimate second-order output probabilities, but not higher-order ones. We show that if one is only able to obtain a reliable estimate of the pairwise co-occurrence probabilities of the emissions, it is still possible to uniquely identify the HMM if the emission probability is sufficiently scattered. We apply our method to hidden topic Markov modeling, and demonstrate that we can learn topics with higher quality if documents are modeled as observations of HMMs sharing the same emission (topic) probability, compared to the simple but widely used bag-of-words model.

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“…This conical hull may be found by a Non-Negative Matrix Factorization algorithm. Following the advice of [26], in our experiments, we used the Successive Projection Algorithm [27].…”

Section: Learning Sp-rfamentioning

confidence: 99%

Learning dialogue dynamics with the method of moments

Barlier

Laroche

Pietquin

2016

2016 IEEE Spoken Language Technology Workshop (SLT)

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In this paper, we introduce a novel framework to encode the dynamics of dialogues into a probabilistic graphical model. Traditionally, Hidden Markov Models (HMMs) would be used to address this problem, involving a first step of handcrafting to build a dialogue model (e.g. defining potential hidden states) followed by applying expectation-maximisation (EM) algorithms to refine it. Recently, an alternative class of algorithms based on the Method of Moments (MoM) has proven successful in avoiding issues of the EM-like algorithms such as convergence towards local optima, tractability issues, initialization issues or the lack of theoretical guarantees. In this work, we show that dialogues may be modeled by SP-RFA, a class of graphical models efficiently learnable within the MoM and directly usable in planning algorithms (such as reinforcement learning). Experiments are led on the Ubuntu corpus and dialogues are considered as sequences of dialogue acts, represented by their Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). We show that a MoM-based algorithm can learn a compact model of sequences of such acts.

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Spectral learning with non negative probabilities for finite state automaton

Cited by 4 publications

References 9 publications

A Stochastic Model for Computer-Aided Human-Human Dialogue

A Stochastic Model for Computer-Aided Human-Human Dialogue

Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling

Learning dialogue dynamics with the method of moments

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