Index of papers in Proc. ACL 2010 that mention

**generative model**

Abstract | Inspired by recent work in summarization, we propose extractive and abstractive caption generation models . |

Abstractive Caption Generation | Despite its simplicity, the caption generation model in (7) has a major drawback. |

Abstractive Caption Generation | After integrating the attachment probabilities into equation (12), the caption generation model becomes: |

Conclusions | Rather than adopting a two-stage approach, where the image processing and caption generation are carried out sequentially, a more general model should integrate the two steps in a unified framework. |

Experimental Setup | In this section we discuss our experimental design for assessing the performance of the caption generation models presented above. |

Image Annotation | It is important to note that the caption generation models we propose are not especially tied |

generative model is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- phrase-based (15)
- TER (10)
- extractive system (6)

Proposed Methods | There are many ways to construct the above mentioned three componen mod ls, i.e., the sentence generative model FED | 513 , the sentence prior model P(Sj), and the loss function L(S,.,Sj). |

Proposed Methods | 4.1 Sentence generative model |

Proposed Methods | In the LM approach, each sentence in a document can be simply regarded as a probabilistic generative model consisting of a unigram distribution (the so-called “bag-0f-words” assumption) for generating the document (Chen et al., 2009): (w) |

generative model is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- loss function (14)
- LM (8)
- CRF (6)

Conclusion | We presented a new generative model of word lists that automatically finds cognate groups from scrambled vocabulary lists. |

Introduction | In this paper, we present a new generative model for the automatic induction of cognate groups given only (1) a known family tree of languages and (2) word lists from those languages. |

Model | In this section, we describe a new generative model for vocabulary lists in multiple related languages given the phylogenetic relationship between the languages (their family tree). |

Model | Figure 1(a) graphically describes our generative model for three Romance languages: Italian, Portuguese, and Spanish.1 In each cognate group, each word W5 is generated from its parent according to a conditional distribution with parameter (pg, which is specific to that edge in the tree, but shared between all cognate groups. |

generative model is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

- unigram (21)
- edit distances (14)
- bigram (9)

Abstract | In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference. |

Background and Motivation | Our approach differs from the early work, in that, we combine a generative hierarchical model and regression model to score sentences in new documents, eliminating the need for building a generative model for new document clusters. |

Introduction | In this paper, we present a novel approach that formulates MDS as a prediction problem based on a two-step hybrid model: a generative model for hierarchical topic discovery and a regression model for inference. |

generative model is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- regression model (12)
- unigram (11)
- topic model (10)

Conclusions and future work | Another potential direction for system improvement would be an integration of our generative model with Bergsma et al.’s (2008) discriminative model — this could be done in a number of ways, including using the induced classes of a topic model as features for a discriminative classifier or using the discriminative classifier to produce additional high-quality training data from noisy unparsed text. |

Introduction | Advantages of these models include a well-defined generative model that handles sparse data well, the ability to jointly induce semantic classes and predicate-specific distributions over those classes, and the enhanced statistical strength achieved by sharing knowledge across predicates. |

Results | For frequent predicate-argument pairs (Seen datasets), Web counts are clearly better; however, the BNC counts are unambiguously superior to LDA and ROOTH-LDA (whose predictions are based entirely on the generative model even for observed items) for the Seen verb-object data only. |

generative model is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- LDA (29)
- topic models (15)
- latent variables (11)

Experiments | These similarity measures were shown to outperform the generative model of Rooth et al. |

Previous Work | On the other hand, generative models produce complete probability distributions of the data, and hence can be integrated with other systems and tasks in a more principled manner (see Sections 4.2.2 and 4.3.1). |

Topic Models for Selectional Prefs. | In the generative model for our data, each relation T has a corresponding multinomial over topics 67., drawn from a Dirichlet. |

generative model is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- topic models (12)
- WordNet (10)
- LDA (8)

Experimental Evaluation | To investigate the generative models , we replace the two phrase translation probabilities and keep the other features identical to the baseline. |

Phrase Model Training | Additionally we consider smoothing by different kinds of interpolation of the generative model with the state-of-the-art heuristics. |

Related Work | For a generative model , (DeNero et al., 2006) gave a detailed analysis of the challenges and arising problems. |

generative model is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- phrase table (33)
- BLEU (19)
- translation probabilities (15)