| Abstract | Another line of work has produced handcrafted rule-based systems to control specific stylistic dimensions, such as politeness and personality. |
| Abstract | We compare our performance to a rule-based generator in the same domain. |
| Evaluation Experiment | Q3: How does PERSONAGE-PE compare to PERSONAGE, a psychologically-informed rule-based generator for projecting extreme personality? |
| Evaluation Experiment | comparison with rule-based results in Section 3.3 suggests that this is not because conscientiousness cannot be exhibited in our domain or manifested in a single utterance, so perhaps this arises from differing perceptions of conscientiousness between the expert and naive judges. |
| Evaluation Experiment | 3.3 Comparison with Rule-Based Generation PERSONAGE is a rule-based personality generator based on handcrafted parameter settings derived from psychological studies. |
| Introduction | Langkilde and Knight (1998) first applied SLMs to statistical natural language generation (SNLG), showing that high quality paraphrases can be generated from an underspecified representation of meaning, by first applying a very undercon-strained, rule-based overgeneration phase, whose outputs are then ranked by an SLM scoring phase. |
| Introduction | In previous work, we presented PERSONAGE, a psychologically-informed rule-based generator based on the Big Five personality model, and we showed that PERSONAGE can project extreme personality on the extraversion scale, i.e. |
| Introduction | Section 3.2 shows that humans accurately perceive the intended variation, and Section 3.3 compares PERSONAGE-PE (trained) with PERSONAGE ( rule-based ; Mairesse and Walker, 2007). |
| Parameter Estimation Models | We test a Naive Bayes classifier (NB), a j48 decision tree (J48), a nearest-neighbor classifier using one neighbor (NN), a Java implementation of the RIPPER rule-based learner (J RIP), the AdaBoost boosting algorithm (ADA), and a support vector machines classifier with a linear kernel (SVM). |
| Experiments | In this section we compare our statistical methods with the preprocessing method and the rule-based methods for measure word generation in a translation task. |
| Experiments | We also compared our method with a well-known rule-based machine translation system —SYSTRAN3. |
| Related Work | Most existing rule-based English-to-Chinese MT systems have a dedicated module handling measure word generation. |
| Related Work | In general a rule-based method uses manually constructed rule patterns to predict measure words. |