Linking adult second language learning and diachronic change:a cautionary note

It has been suggested that the morphological complexity of a language is negatively correlated with the size of its population of speakers. This relationship may be driven by the proportion of non-native speakers, among other things, and reflects adaptations to learning constraints imposed by adult language learners. Here we sound a note of caution with respect to these claims by arguing that (a) morphological complexity is defined in somewhat contradictory ways and hence not straightforward to measure, and (b) there is insufficient evidence to suggest that children’s cognitive limitations support mechanisms beneficial for learning of complex morphology relative to adults. We suggest that considering the informational value of morphological cues may be a better way to capture learnability of morphology. To settle the issue of how age related constraints on learning might impact language change, more cross-linguistic studies comparing learning trajectories of different second languages and laboratory experiments examining language transmission in children and adults are needed

From holism to compositionality: memes and the evolution of segmentation, syntax, and signification in music and language

Steven Mithen argues that language evolved from an antecedent he terms “Hmmmmm, [meaning it was] Holistic, manipulative, multi-modal, musical and mimetic”. Owing to certain innate and learned factors, a capacity for segmentation and cross-stream mapping in early Homo sapiens broke the continuous line of Hmmmmm, creating discrete replicated units which, with the initial support of Hmmmmm, eventually became the semantically freighted words of modern language. That which remained after what was a bifurcation of Hmmmmm arguably survived as music, existing as a sound stream segmented into discrete units, although one without the explicit and relatively fixed semantic content of language. All three types of utterance – the parent Hmmmmm, language, and music – are amenable to a memetic interpretation which applies Universal Darwinism to what are understood as language and musical memes. On the basis of Peter Carruthers’ distinction between ‘cognitivism’ and ‘communicativism’ in language, and William Calvin’s theories of cortical information encoding, a framework is hypothesized for the semantic and syntactic associations between, on the one hand, the sonic patterns of language memes (‘lexemes’) and of musical memes (‘musemes’) and, on the other hand, ‘mentalese’ conceptual structures, in Chomsky’s ‘Logical Form’ (LF)

Adapting the Core Language Engine to French and Spanish

We describe how substantial domain-independent language-processing systems for French and Spanish were quickly developed by manually adapting an existing English-language system, the SRI Core Language Engine. We explain the adaptation process in detail, and argue that it provides a fairly general recipe for converting a grammar-based system for English into a corresponding one for a Romance language.Comment: 9 pages, aclap.sty; to appear in NLP+IA 96; see also http://www.cam.sri.com

QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships

Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges. We present QuaRel, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them. The dataset has 2771 questions relating 19 different types of quantities. For example, "Jenny observes that the robot vacuum cleaner moves slower on the living room carpet than on the bedroom carpet. Which carpet has more friction?" We contribute (1) a simple and flexible conceptual framework for representing these kinds of questions; (2) the QuaRel dataset, including logical forms, exemplifying the parsing challenges; and (3) two novel models for this task, built as extensions of type-constrained semantic parsing. The first of these models (called QuaSP+) significantly outperforms off-the-shelf tools on QuaRel. The second (QuaSP+Zero) demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships without requiring additional training data, something not possible with previous models. This work thus makes inroads into answering complex, qualitative questions that require reasoning, and scaling to new relationships at low cost. The dataset and models are available at http://data.allenai.org/quarel.Comment: 9 pages, AAAI 201

Learning Sentence-internal Temporal Relations

In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like after", which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and approximate data (i.e., main and subordinate clauses) to predict intra-sentential relations present in TimeBank, a corpus annotated rich temporal information. Our experiments compare and contrast several probabilistic models differing in their feature space, linguistic assumptions and data requirements. We evaluate performance against gold standard corpora and also against human subjects

THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes