Modeling Topic and Role Information in Meetings using the Hierarchical Dirichlet Process

Abstract. In this paper, we address the modeling of topic and role information in multiparty meetings, via a nonparametric Bayesian model called the hierarchical Dirichlet process. This model provides a powerful solution to topic modeling and a flexible framework for the incorporation of other cues such as speaker role information. We present our modeling framework for topic and role on the AMI Meeting Corpus, and illustrate the effectiveness of the approach in the context of adapting a baseline language model in a large-vocabulary automatic speech recognition system for multiparty meetings. The adapted LM produces significant improvements in terms of both perplexity and word error rate.

Direct observation of a Fermi liquid-like normal state in an iron-pnictide superconductor

There are two prerequisites for understanding high-temperature (high-T$_c$) superconductivity: identifying the pairing interaction and a correct description of the normal state from which superconductivity emerges. The nature of the normal state of iron-pnictide superconductors, and the role played by correlations arising from partially screened interactions, are still under debate. Here we show that the normal state of carefully annealed electron-doped BaFe$_{2-x}$Co$_{x}$As$_2$ at low temperatures has all the hallmark properties of a local Fermi liquid, with a more incoherent state emerging at elevated temperatures, an identification made possible using bulk-sensitive optical spectroscopy with high frequency and temperature resolution. The frequency dependent scattering rate extracted from the optical conductivity deviates from the expected scaling $M_{2}(\omega,T)\propto(\hbar\omega)^{2}+(p\pi k_{B}T)^{2}$ with $p\approx$ 1.47 rather than $p$ = 2, indicative of the presence of residual elastic resonant scattering. Excellent agreement between the experimental results and theoretical modeling allows us to extract the characteristic Fermi liquid scale $T_{0}\approx$ 1700 K. Our results show that the electron-doped iron-pnictides should be regarded as weakly correlated Fermi liquids with a weak mass enhancement resulting from residual electron-electron scattering from thermally excited quasi-particles.Comment: 6+9pages, 3+9 figures To be published in Scientific Report

A Parallel Training Algorithm for Hierarchical Pitman-Yor Process Language Models

The Hierarchical Pitman Yor Process Language Model (HPYLM) is a Bayesian language model based on a non-parametric prior, the Pitman-Yor Process. It has been demonstrated, both theoretically and practically, that the HPYLM can provide better smoothing for language modeling, compared with state-of-the-art approaches such as interpolated Kneser-Ney and modified Kneser-Ney smoothing. However, estimation of Bayesian language models is expensive in terms of both computation time and memory; the inference is approximate and requires a number of iterations to converge. In this paper, we present a parallel training algorithm for the HPYLM, which enables the approach to be applied in the context of automatic speech recognition, using large training corpora with large vocabularies. We demonstrate the effectiveness of the proposed algorithm by estimating language models from corpora for meeting transcription containing over 200 million words, and observe significant reductions in perplexity and word error rate

Structure-Substrate Binding Relationships of HIV-1 Reverse Transcriptase

Human Immunodeficiency Virus, type 1 (HIV-1), is the causative agent of the Acquired Immunodeficiency Syndrome (AIDS). HIV-1 reverse transcriptase (RT), a heterodimer p66/p51, has been the major target for treatment of AIDS. The significance of the p51 subunit and the RNase H domain of p66 in terms of their influence on the RNA-dependent DNA synthesis was investigated. Clones of the wildtype HIV-1 RT subunits, p66 and p51, and a recombinant C-terminal deletion mutant, p64, [Barr, P. J. (1987) Bio/Technoloav 5, 486-489] were employed to study the structure-substrate binding relationships of HIV-1 RT. The activity assays of RNA-dependent DNA synthesis on both poly(rA)(dT) and a random base RNA template hybridized with a DNA oligomer showed that p51 significantly affects the enzyme activity. The increase in processivity by p51 in the p66/p51 heterodimer was also demonstrated. These observations suggested that the integrity of p51 is important in subunit-interactions for maintaining a favorable conformation of the enzyme for optimal function. C-terminal deletion in p66 was seen to decrease the processivity. The dissociation constant (Kd) for poly(rA)(dT) obtained by nitrocellulose binding assays suggested that the processivity of HIV 1 RT on poly(rA)(dT) correlated with the affinity for the substrate. The processivity of RT on RNA335-DNA20 was seen to be affected by the pause sites observed on the autoradiograms. The pauses of DNA synthesis tended to occur at positions of template containing poly G-C sequences. The order of processivity observed on RNA335-DNA20 was p64/p64, p66/p66 \u3c p64/p51 \u3c p66/p51. The C-terminal deletion in p66 was shown to affect the ability to extend the DNA strand on RNA template. In those non-wildtype forms of HIV-1 RT (p66/p66, p64/p64, and p64/p51), the affinity for primer-template seemed to be sensitive to the structure of the RNA template as seen when comparing Kds between poly(rA)(dT) and RNA335-DNA20. The wildtype enzyme, p66/p51, appeared to have a similar affinity for both substrates

Power Law Discounting for N-Gram Language Models

We present an approximation to the Bayesian hierarchical Pitman-Yor process language model which maintains the power law distribution over word tokens, while not requiring a computationally expensive approximate inference process. This approximation, which we term power law discounting, has a similar computational complexity to interpolated and modified Kneser-Ney smoothing. We performed experiments on meeting transcription using the NIST RT06s evaluation data and the AMI corpus, with a vocabulary of 50,000 words and a language model training set of up to 211 million words. Our results indicate that power law discounting results in statistically significant reductions in perplexity and word error rate compared to both interpolated and modified Kneser-Ney smoothing, while producing similar results to the hierarchical Pitman-Yor process language model

Hierarchical Bayesian Language Models for Conversational Speech Recognition

Traditional n-gram language models are widely used in state-of-the-art large vocabulary speech recognition systems. This simple model suffers from some limitations, such as overfitting of maximum-likelihood estimation and the lack of rich contextual knowledge sources. In this paper, we exploit a hierarchical Bayesian interpretation for language modeling, based on a nonparametric prior called the Pitman--Yor process. This offers a principled approach to language model smoothing, embedding the power-law distribution for natural language. Experiments on the recognition of conversational speech in multiparty meetings demonstrate that by using hierarchical Bayesian language models, we are able to achieve significant reductions in perplexity and word error rate

Using Participant Role in Multiparty Meetings as Prior Knowledge for Nonparametric Topic Modeling

In this paper we introduce our attempts to incorporate the participant role information in multiparty meetings for document modeling using the hierarchical Dirichlet process. The perplexity and automatic speech recognition results demonstrate that the participant role information is a promising prior knowledge source to be combined with language models for automatic speech recognition and interaction modeling for multiparty meetings

Using the Internet to Create Positive Social Changes: Case Studies in China

In recent years, companies have been increasingly under pressure to deliver programs that can create both business value and social value. Building on the positive social change framework developed by Stephan et al., this paper uses two case studies (Gongyi Baobei and Jutudi) of the Alibaba Group, a leading Internet company in China, to investigate how companies can use the Internet to bring about positive social changes (PSC) to target groups. Our focus is placed on the nature of projects, i.e., surface-level and deep-level PSC projects. Our decision to use different case studies from the same company is based on the assumption that the enabling effects of internal organizational practices should be similar. To be more specific, we want to study the link between PSC projects and the company’s existing businesses, the role of the Internet in raising customers’ awareness and participation in the programs, and the change mechanism designed and implemented to bring positive social changes to customers. Data were collected through interviews and literature review. Our research provides empirical evidence to show a deep-level PSC project (i.e., Jutudi) can be very different from a surface-level PSC project (i.e., Gongyi Baobei) in terms of the reliance on existing business operations and the design of change mechanisms. Our research limitations and direction for future research will also be discussed

Gaze Controlled Human-Computer Interface

The goal of the Gaze Controlled Human Computer Interface project is to design and construct a non-invasive gaze-tracking system that will determine where a user is looking on a computer screen in real time. To accomplish this, a fixed illumination source consisting of Infrared (IR) Light Emitting Diodes (LEDs) is used to produce corneal reflections on the user’s eyes. These reflections are captured with a video camera and compared to the relative location of the user’s pupils. From this comparison, a correlation matrix can be created and the approximate location of the screen that the user is looking at can be determined. The final objective is to allow the user to manipulate a cursor on the computer screen simply by looking at different boxes in a grid on the monitor. The project includes design of the hardware setup to provide a suitable environment for glint detection, image processing of the user’s eyes to determine pupil location, the implementation of a probabilistic algorithm to determine an appropriate matrix transformation, and performance analysis on various users