Background | The baseline generative model we use for reranking employs the unsupervised PCFG induction approach introduced by Kim and Mooney (2012). |
Background | Our proposed reranking model is used to discriminatively reorder the top parses produced by this generative model . |
Introduction | Since their system employs a generative model , discriminative reranking (Collins, 2000) could p0-tentially improve its performance. |
Introduction | By training a discriminative classifier that uses global features of complete parses to identify correct interpretations, a reranker can significantly improve the accuracy of a generative model . |
Modified Reranking Algorithm | In reranking, a baseline generative model is first trained and generates a set of candidate outputs for each training example. |
Modified Reranking Algorithm | The approach requires three subcomponents: l) a GEN function that returns the list of top n candidate parse trees for each NL sentence produced by the generative model , 2) a feature function (I) that maps a NL sentence, 6, and a parse tree, y, into a real-valued feature vector (19(6, 3/) 6 Rd, and 3) a reference parse tree that is compared to the highest-scoring parse tree during training. |
Related Work | Discriminative reranking is a common machine learning technique to improve the output of generative models . |
Reranking Features | This section describes the features (I) extracted from parses produced by the generative model and used to rerank the candidates. |
Reranking Features | Certainty assigned by the base generative model . |
Introduction | We present a new, generative model specialized to transcribing printing-press era documents. |
Model | We take a generative modeling approach inspired by the overall structure of the historical printing process. |
Model | Our generative model , which is depicted in Figure 3, reflects this process. |
Related Work | In the NLP community, generative models have been developed specifically for correcting outputs of OCR systems (Kolak et al., 2003), but these do not deal directly with images. |
Results and Analysis | As noted earlier, one strength of our generative model is that we can make the values of certain pixels unobserved in the model, and let inference fill them in. |
Background and related work | They propose a pipeline architecture involving two separate generative models , one for word-segmentation and one for phonological variation. |
Background and related work | This permits us to develop a joint generative model for both word segmentation and variation which we plan to extend to handle more phenomena in future work. |
Conclusion and outlook | A major advantage of our generative model is the ease and transparency with which its assumptions can be modified and extended. |
The computational model | One of the advantages of an explicitly defined generative model such as ours is that it is straightforward to gradually extend it by adding more cues, as we point out in the discussion. |
Abstract | As an illustrative case, we study a generative model for dependency parsing. |
Discussion | In principle, our branch-and-bound method can approach e-optimal solutions to Viterbi training of locally normalized generative models , including the NP-hard case of grammar induction with the DMV. |
The Constrained Optimization Task | Other locally normalized log-linear generative models (Berg-Kirkpatrick et al., 2010) would have a similar formulation. |
The Constrained Optimization Task | This generative model defines a joint distribution over the sentences and their dependency trees. |
Abstract | We use a Bayesian generative model to capture relevant natural language phenomena and translate the English specification into a specification tree, which is then translated into a C++ input parser. |
Model | We combine these two kinds of information into a Bayesian generative model in which the code quality of the specification tree is captured by the prior probability P (t) and the feature observations are encoded in the likelihood probability P (w|t). |
Model | We assume the generative model operates by first generating the model parameters from a set of Dirichlet distributions. |
Model | 0 Generating Model Parameters: For every pair of feature type f and phrase tag 2, draw a multinomial distribution parameter 63 from a Dirichlet prior P(6§;). |
Conclusion | A novel technique was also proposed to rank n-gram phrases where relevance based ranking was used in conjunction with a semi-supervised generative model . |
Introduction | We employ a semi-supervised generative model called JTE-P to jointly model AD-expressions, pair interactions, and discussion topics simultaneously in a single framework. |
Model | JTE-P is a semi-supervised generative model motivated by the joint occurrence of expression types (agreement and disagreement), topics in discussion posts, and user pairwise interactions. |
Model | Like most generative models for text, a post (document) is viewed as a bag of n-grams and each n-gram (word/phrase) takes one value from a predefined vocabulary. |
Evaluation with Native-Speakers | With respect to H, our discriminative models achieve from 0.12 to 0.2 higher agreement than baselines, indicating that the discriminative models can generate sound distractors more effectively than generative models . |
Evaluation with Native-Speakers | The lower H on generative models may be because the distractors are semantically too close to the target (correct answer) as following examples: |
Evaluation with Native-Speakers | As a result, the quiz from generative models is not reliable since both published and issued are correct. |
Proposed Method | We rank the candidates by a generative model to consider the surrounding context (e.g. |
Abstract | We introduce Distributional Semantic Hidden Markov Models, a novel variant of a hidden Markov model that integrates these two approaches by incorporating contextualized distributional semantic vectors into a generative model as observed emissions. |
Introduction | Second, the contextualization process allows the semantic vectors to implicitly encode disambiguated word sense and syntactic information, without further adding to the complexity of the generative model . |
Related Work | Other related generative models include topic models and structured versions thereof (Blei et al., 2003; Gruber et al., 2007; Wallach, 2008). |
Generative state tracking | In contrast to generative models , discriminative approaches to dialog state tracking directly predict the correct state hypothesis by leveraging discrim-inatively trained conditional models of the form (9(9) 2 P(g| f), where f are features extracted from various sources, e.g. |
Introduction | (2010); Thomson and Young (2010)) use generative models that capture how the SLU results are generated from hidden dialog states. |
Introduction | As an illustration, in Figure 1, a generative model might fail to assign the highest score to the correct hypothesis (61C) after the second turn. |