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

Grounding language models in spatiotemporal context

Natural language is rich and varied, but also highly structured. The rules of grammar are a primary source of linguistic regularity, but there are many other factors that govern patterns of language use. Language models attempt to capture linguistic regularities, typically by modeling the statistics of word use, thereby folding in some aspects of grammar and style. Spoken language is an important and interesting subset of natural language that is temporally and spatially grounded. While time and space may directly contribute to a speaker’s choice of words, they may also serve as indicators for communicative intent or other contextual and situational factors. To investigate the value of spatial and temporal information, we build a series of language models using a large, naturalistic corpus of spatially and temporally coded speech collected from a home environment. We incorporate this extralinguistic information by building spatiotemporal word classifiers that are mixed with traditional unigram and bigram models. Our evaluation shows that both perplexity and word error rate can be significantly improved by incorporating this information in a simple framework. The underlying principles of this work could be applied in a wide range of scenarios in which temporal or spatial information is available

Compositional Morphology for Word Representations and Language Modelling

This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic language model. Our approach is evaluated in the context of log-bilinear language models, rendered suitably efficient for implementation inside a machine translation decoder by factoring the vocabulary. We perform both intrinsic and extrinsic evaluations, presenting results on a range of languages which demonstrate that our model learns morphological representations that both perform well on word similarity tasks and lead to substantial reductions in perplexity. When used for translation into morphologically rich languages with large vocabularies, our models obtain improvements of up to 1.2 BLEU points relative to a baseline system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning (ICML

Decision Tree-based Syntactic Language Modeling

Statistical Language Modeling is an integral part of many natural language processing applications, such as Automatic Speech Recognition (ASR) and Machine Translation. N-gram language models dominate the field, despite having an extremely shallow view of language---a Markov chain of words. In this thesis, we develop and evaluate a joint language model that incorporates syntactic and lexical information in a effort to ``put language back into language modeling.'' Our main goal is to demonstrate that such a model is not only effective but can be made scalable and tractable. We utilize decision trees to tackle the problem of sparse parameter estimation which is exacerbated by the use of syntactic information jointly with word context. While decision trees have been previously applied to language modeling, there has been little analysis of factors affecting decision tree induction and probability estimation for language modeling. In this thesis, we analyze several aspects that affect decision tree-based language modeling, with an emphasis on syntactic language modeling. We then propose improvements to the decision tree induction algorithm based on our analysis, as well as the methods for constructing forest models---models consisting of multiple decision trees. Finally, we evaluate the impact of our syntactic language model on large scale Speech Recognition and Machine Translation tasks. In this thesis, we also address a number of engineering problems associated with the joint syntactic language model in order to make it tractable. Particularly, we propose a novel decoding algorithm that exploits the decision tree structure to eliminate unnecessary computation. We also propose and evaluate an approximation of our syntactic model by word n-grams---the approximation that makes it possible to incorporate our model directly into the CDEC Machine Translation decoder rather than using the model for rescoring hypotheses produced using an n-gram model