The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

A unified theory of counterfactual reasoning

A successful theory of causal reasoning should be able to account for inferences about counterfactual scenarios. Pearl (2000) has developed a formal account of causal reasoning that has been highly influential but that suffers from at least two limitations as an account of counterfactual reasoning: it does not distinguish between counterfactual observations and counterfactual interventions, and it does not accommodate backtracking counterfactuals. We present an extension of Pearl’s account that overcomes both limitations. Our model provides a unified treatment of counterfactual interventions and backtracking counterfactuals, and we show that it accounts for data collected by Sloman and Lagnado (2005) and Rips (2010). In addition to reasoning about actual states of affairs, humans find it natural to reason about what might have been

Musicians are better at learning non-native sound contrasts even in non-tonal languages

It is very difficult for adults to perceive phonetic contrasts in their non-native language. In this study we explored the effects of phonetic training for different populations of people (musicians and non-musicians)and with different kinds of phoneme contrast (timing-based, like the Hindi /g/-/k/ contrast, and pitch-based, like the Mandarin /l/-/l/ tonal contrast). We found that musicians had superior perception for both contrasts, not just the pitch-based one. For both phonemes, training had little to no effect. We consider the implications of this for first and second language acquisition.Amy Perfors and Jia Hoong On

Expertise and the wisdom of crowds: Whose judgments to trust and when

The Wisdom of Crowds describes the fact that aggregating a group’s estimate regarding unknown values is often a better strategy than selecting even an expert’s opinion. The efficacy of this strategy, however, depends on biases being nonsystematic and everyone being able to make a meaningful assessment. In situations where these conditions do not hold, expertise seems more likely to produce the best outcome. Amateurs and professional judgments are examined in a subjective domain – reviews of shows from an Arts festival – asking which group provides better information to the potential theatre-goer. In conclusion, while following the crowd produces good results, where a smaller number of reviews are available, taking expertise into account improves their usefulness and discrimination between shows.Matthew B. Wels

Probability matching vs over-regularization in language: participant behavior depends on their interpretation of the task

In a variety of domains, children have been observed to overregularize inconsistent input, while adults are more likely to “probability match” to any inconsistency. Many explanations for this have been offered, usually relating to cognitive differences between children and adults. Here we explore an additional possibility: that differences in the social assumptions participants bring to the experiment can drive differences in over-regularization behavior. We explore this in the domain of language, where assumptions about error and communicative purpose might have a large effect. Indeed, we find that participants who experience less pressure to be “correct” and who have more reason to believe that any inconsistencies do not correspond to an underlying regularity do over-regularize more. Implications for language acquisition in children and adults are discussed.Amy Perfor

Anticipating changes: adaptation and extrapolation in category learning

Our world is a dynamic one: many kinds of objects and events change markedly over time. Despite this, most theories about concepts and categories are either insensitive to time-based variation, or treat people’s sensitivity to change as a result of process-level characteristics (like memory limits, captured by weighting more recent items more highly) that produce irrational order effects during learning. In this paper we use two experiments and nine computational models to explore how people learn in a changing environment. We find, first, that people adapt to change during a category learning task; and, second, that this adaptation stems not only from weighting more recent items more highly, but also from forming sensible anticipations about the nature of the change.Daniel J. Navarro and Amy Perfor

Strong structure in weak semantic similarity: a graph based account

Research into word meaning and similarity structure typically focus on highly related entities like CATS and MICE. However, most items in the world are only weakly related. Does our representation of the world encode any information about these weak relationships? Using a three-alternative forced-choice similarity task, we investigate to what extent people agree on the relationships underlying words that are only weakly related. These experiments show systematic preferences about which items are perceived as most similar. A similarity measure based on semantic network graphs gives a good account for human ratings of weak similarity.Simon De Deyne, Daniel J. Navarro, Amy Perfors and Gert Storm

Marking Topic or Marking Case: A Comparative Investigation of Heritage Japanese and Heritage Korean

In this paper, we examine the relationship between grammatical and discourse-related domains of linguistic organization in heritage speakers by comparing their knowledge of categories mediated at different structural levels: grammatical case marking, which is mediated within the structure of the clause, and the marking of information structure, grammatically mediated at the syntax-discourse interface. To this end, we examine the knowledge of case and topic particles in heritage speakers and L2 learners of Japanese and Korean as assessed through a series of rating tasks. We find that heritage speakers in both languages experience different degrees of difficulty with elements that belong to different linguistic modules: phenomena which involve semantic and discourse computation are found to be more difficult than phenomena governed primarily by structural syntactic constraints.Linguistic