When a stone tries to climb up a slope:The interplay between lexical and perceptual animacy in referential choices

Several studies suggest that referential choices are influenced by animacy. On the one hand, animate referents are more likely to be mentioned as subjects than inanimate referents. On the other hand, animate referents are more frequently pronominalized than inanimate referents. These effects have been analyzed as effects of conceptual accessibility. In this paper, we raise the question whether these effects are driven only by lexical concepts, such that referents described by animate lexical items (e.g., “toddler”) are more accessible than referents described by inanimate lexical items (e.g., “shoe”), or can also be influenced by context-derived conceptualizations, such that referents that are perceived as animate in a particular context are more accessible than referents that are not. In two animation-retelling experiments, conducted in Dutch, we investigated the influence of lexical and perceptual animacy on the choice of referent and the choice of referring expression. If the effects of animacy are context-dependent, entities that are perceived as animate should yield more subject references and more pronouns than entities that are perceived as inanimate, irrespective of their lexical animacy. If the effects are tied to lexical concepts, entities described with animate lexical items should be mentioned as the subject and pronominalized more frequently than entities described with inanimate lexical items, irrespective of their perceptual animacy. The results show that while only lexical animacy appears to affect the choice of subject referent, perceptual animacy may overrule lexical animacy in the choice of referring expression. These findings suggest that referential choices can be influenced by conceptualizations based on the perceptual context

Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.Comment: 14 pages, published at TACL, 2017, Volume-5, Pg 31-44, 201

Processing at the syntax-discourse interface in second language acquisition

The Interface Hypothesis (Sorace and Filiaci, 2006) conjectures that adult second language learners (L2 learners) who have reached near-native levels of proficiency in their second language exhibit difficulties at the interface between syntax and other cognitive domains, most notably at the syntax-discourse interface. However, research in this area was limited, in that the data were offline, and thus unable to provide evidence for the nature of the deficit shown by L2 learners. This thesis presents online data which address the question of the underlying nature of the difficulties observed in L2 learners at the syntaxdiscourse interface. This thesis has extended work on the syntax-discourse interface in L2 learners by investigating the acquisition of two phenomena at the syntax-discourse interface in German: the role of word order and pronominalization with respect to information structure (Experiments 1-3), and the antecedent preferences of anaphoric demonstrative (the der, die, das series homophonous with the definite article) and personal pronouns (the er, sie, es series) (Experiments 4- 8). Crucially, this work has used an on-line methodology, the visual-world paradigm, which allows an insight into the incremental interpretation of interface phenomena in real-time processing. The data from these experiments show that L2 learners have difficulty integrating different sources of information in real-time comprehension efficiently, supporting the Interface Hypothesis. However, the nature of the processing difficulties which L2 learners demonstrate in on-line processing was not determined by these studies, resulting in the question: are L2 learners’ difficulties a result of a limitation of processing resources, or the inability to deploy those resources effectively? A novel dualtask experiment (Experiment 9), in which native speakers of German were placed under processing load simulated the results previously obtained for L2 learners. It is concluded that syntactic dependencies were constrained by resource limitation, whereas discourse based dependencies were constrained by processing resource allocation

Demonstratives in discourse

This volume explores the use of demonstratives in the structuring and management of discourse, and their role as engagement expressions, from a crosslinguistic perspective. It seeks to establish which types of discourse-related functions are commonly encoded by demonstratives, beyond the well-established reference-tracking and deictic uses, and also investigates which members of demonstrative paradigms typically take on certain functions. Moreover, it looks at the roles of non-deictic demonstratives, that is, members of the paradigm which are dedicated e.g. to contrastive, recognitional, or anaphoric functions and do not express deictic distinctions. Several of the studies also focus on manner demonstratives, which have been little studied from a crosslinguistic perspective. The volume thus broadens the scope of investigation of demonstratives to look at how their core functions interact with a wider range of discourse functions in a number of different languages. The volume covers languages from a range of geographical locations and language families, including Cushitic and Mande languages in Africa, Oceanic and Papuan languages in the Pacific region, Algonquian and Guaykuruan in the Americas, and Germanic, Slavic and Finno-Ugric languages in the Eurasian region. It also includes two papers taking a broader typological approach to specific discourse functions of demonstratives

Demonstratives in discourse

This volume explores the use of demonstratives in the structuring and management of discourse, and their role as engagement expressions, from a crosslinguistic perspective. It seeks to establish which types of discourse-related functions are commonly encoded by demonstratives, beyond the well-established reference-tracking and deictic uses, and also investigates which members of demonstrative paradigms typically take on certain functions. Moreover, it looks at the roles of non-deictic demonstratives, that is, members of the paradigm which are dedicated e.g. to contrastive, recognitional, or anaphoric functions and do not express deictic distinctions. Several of the studies also focus on manner demonstratives, which have been little studied from a crosslinguistic perspective. The volume thus broadens the scope of investigation of demonstratives to look at how their core functions interact with a wider range of discourse functions in a number of different languages. The volume covers languages from a range of geographical locations and language families, including Cushitic and Mande languages in Africa, Oceanic and Papuan languages in the Pacific region, Algonquian and Guaykuruan in the Americas, and Germanic, Slavic and Finno-Ugric languages in the Eurasian region. It also includes two papers taking a broader typological approach to specific discourse functions of demonstratives

Learning to tell tales: automatic story generation from Corpora

Automatic story generation has a long-standing tradition in the field of Artificial Intelligence. The ability to create stories on demand holds great potential for entertainment and education. For example, modern computer games are becoming more immersive, containing multiple story lines and hundreds of characters. This has substantially increased the amount of work required to produce each game. However, by allowing the game to write its own story line, it can remain engaging to the player whilst shifting the burden of writing away from the game’s developers. In education, intelligent tutoring systems can potentially provide students with instant feedback and suggestions of how to write their own stories. Although several approaches have been introduced in the past (e.g., story grammars, story schema and autonomous agents), they all rely heavily on handwritten resources. Which places severe limitations on its scalability and usage. In this thesis we will motivate a new approach to story generation which takes its inspiration from recent research in Natural Language Generation. Whose result is an interactive data-driven system for the generation of children’s stories. One of the key features of this system is that it is end-to-end, realising the various components of the generation pipeline stochastically. Knowledge relating to the generation and planning of stories is leveraged automatically from corpora and reformulated into new stories to be presented to the user. We will also show that story generation can be viewed as a search task, operating over a large number of stories that can be generated from knowledge inherent in a corpus. Using trainable scoring functions, our system can search the story space using different document level criteria. In this thesis we focus on two of these, namely, coherence and interest. We will also present two major paradigms for generation through search, (a) generate and rank, and (b) genetic algorithms. We show the effects on perceived story interest, fluency and coherence that result from these approaches. In addition, we show how the explicit use of plots induced from the corpus can be used to guide the generation process, providing a heuristically motivated starting point for story search. We motivate extensions to the system and show that additional modules can be used to improve the quality of the generated stories and overall scalability. Finally we highlight the current strengths and limitations of our approach and discuss possible future approaches to this field of research