Connectionist Modeling of Experience-based Effects in Sentence ...

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4.4 Forgetting Effects GPE 0.0 0.2 0.4 0.6 0.8 1.0 German without commas (epoch 2) V3 V2 V1 post-V1 Region drop-V2 no-drop GPE 0.0 0.2 0.4 0.6 0.8 1.0 German without commas (epoch 3) V3 V2 V1 post-V1 Region drop-V2 no-drop Figure 4.9: Simulation 4b: German doubly-embedded ORC without commas. The graphic shows the GPE value on the three verbs and the subsequent region of the grammatical (nod-rop) and ungrammatical (drop-V2) condition after two and 3 epochs of training. however, should still cause better predictions for the grammatical condition in German than in English. Simulation 4b tested SRNs trained on a comma-free German grammar. (29) Example test sentences: a. der Polizist den der Mensch den die Passanten treffen ruft verspottet den Jungen . (no-drop) b. der Passant den der Junge den der Polizist ruft beschimpft die Passanten . (drop-V2) Results of 4b The GPE values of the simulation involving German without commas (figure 4.9) show a similar pattern as in English without commas. In the first epoch a drop-V2 preference was found in a small effect on V1 and a very pronounced effect on the region after. After completed training, V1 and post-V1 show a similar sized drop-V2 advantage. Looking at the data, the comma aspect seems to be the most relevant one. Surprisingly the regularity of verb-final structures does not seem to support correct predictions in German more than in English. Rather the more regular application of commas in German has an extensively facilitating effect on both conditions, slightly more on the grammatical. The impact of comma usage and a comparison of the results to human data will be the topic of the next section. 79
4.4.4 Discussion Chapter 4 Two SRN Prediction Studies The results of simulation 3a (English without commas) and 4a (German with commas) were consistent with empirical studies (Gibson and Thomas, 1999; Christiansen and MacDonald, 1999; Vasishth et al., 2008), suggesting a difference in forgetting behavior between German and English. A reliable grammaticality preference in German as observed in Vasishth et al. (2008) could, however, not be replicated. Simulation 3a is perfectly consistent with the simulation of the forgetting effect in Christiansen and Chater (1999) and the human data from Vasishth et al. The results prove that not only the limited grammar used in Christiansen and Chater (1999) predicts the forgetting effect but also the more complex grammar used here. The inherent architectural constraints of SRNs predict a forgetting effect in English doubly embedded ORCs. The insertion of commas in simulation 3b had an effect on the predictions, whereas in contrast, the study by Vasishth and colleagues showed no effect. The missing effect of commas in their study could be explained by the fact that English readers are not familiar with using commas in that way. On the other hand, the SRNs were trained on corpora containing commas and, thus, had learned how to use them as structural cues. The remaining drop-V2 preference on V1 still shows a certain consistency between the simulation and empirical data. More importantly, the model makes different predictions in German. Simulation 4a shows a similar performance on grammatical and ungrammatical sentences. In comparison, the Vasishth et al. study found in fact faster reading times in the grammatical condition for German readers. This is not predicted by the model, but it is the difference to English that is important here. Surprisingly, the SRNs trained on German without commas performed no better than the SRNs trained on English without commas. This yields a comma × language interaction. The greater effect of commas in German is explainable by the different comma patterns in both languages (see examples 25 and 27). So, the different predictions regarding the forgetting effect seem to be caused only indirectly by word order regularities. In particular, the consistent center-embedding (or counting-recursion) of commas in German makes them a reliable predictor, whereas this is not the case in English. The word order itself, however, did not have the expected effect, as experiment 4b shows. Controlling for the comma effect the head-finiteness of SRCs in the simplified German grammar does not increase the performance on double center-embedding on V1 and post-V1. Maybe doubly-embedded RCs are too rare in the corpus to cause an effect. Another explanation could be the particularly increased complexity in the prediction of German embedded RCs. In a German RC the cues establishing the agreement of the embedded verb are very subtle compared to English. In English the word order of ‘who NP VP’ versus ‘who VP NP’ decides the agreement, whereas in German there are several possible pairings of der, den, and die, that determine the verb agreement. Thus, the verb agreement prediction requires a complex representation of previous input. Given the architectural limits of the network, highly complex representations are in a tradeoff with memory span. That means that distant dependencies and verb predictions are very hard to maintain. In other words the trace 80
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Connectionist Modeling of Experienc
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Acknowledgments I am grateful to Be
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Contents 3.3.4 Summary . . . . . .
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List of Tables 2.1 Languages with s
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Chapter 1 Preliminaries and the ACT
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Chapter 1 Preliminaries (1) a. The
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1.3 Psycholinguistic Aspects Chapte
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Chapter 1 Preliminaries referrentia
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Chapter 1 Preliminaries empirically
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Chapter 1 Preliminaries rial, in Le
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Chapter 1 Preliminaries are limited
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Chapter 1 Preliminaries predictor.
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Chapter 2 Issues in Relative Clause
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Page 35 and 36: Chapter 2 Issues in Relative Clause
Page 45 and 46: clear predictions for head-final RC
Page 51 and 52: Reading time [ms] 400 600 800 1000
Page 55 and 56: OUTPUT PUT PLAN Chapter 3 Connectio
Page 57 and 58: Chapter 3 Connectionist Modelling o
Page 63 and 64: German Word Order Chapter 3 Connect
Page 65 and 66: Length-Adjusted Reading Time (ms) C
Page 69 and 70: Chapter 4 Two SRN Prediction Studie
Page 71 and 72: 4.1.3 Training and Testing Chapter
Page 73 and 74: GPE 0.0 0.2 0.4 0.6 0.8 1.0 English
Page 75 and 76: (23) [V1 [N1 V2 de ORC ] N2 de SRC
Page 85: (27) German with commas: a. SRC: S1
Page 91 and 92: Bibliography M. H. Christiansen. Th
Page 93 and 94: Bibliography E. Gibson and J. Thoma
Page 95 and 96: Bibliography J. W. King and M. Kuta
Page 97 and 98: Bibliography F. Reali and M. H. Chr
Page 99 and 100: Appendix A Statistics SRC ORC regio
Page 101 and 102: Appendix B Grammars B.1 English (wr
Page 103 and 104: B.2 German {numREL, Nnom, RC RCpure
Page 105 and 106: B.3 Mandarin Rel : SRC (0.85) | ORC
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