Learning Object Names in Real Time with Little Data.

Learning Object Names in Real Time with Little Data Jon Scott Stevens (jonsteve@ling.upenn.edu) Department of Linguistics, 255 S. 36th Street Philadelphia, PA 19104 USA Previous Work Abstract Children are able to learn words from context, often taking entire sentences as input and breaking that input down to create word-to-meaning mappings. In addition to this task, which is far from trivial, children must filter out an infi- nite number of erroneous but logically possible hypotheses of word meaning, as Quine (1960) famously noticed. The quantum leap between considering each member of an infi- nite set (an impossible task) and considering each member of a finite set, no matter how large, is the basis for the claim that word learning relies fundamentally on innately given hy- pothesis space constraints. The question, then, is not whether learning is constrained, but how it is constrained. We take computational models to be tests of purported answers to this question. As such, models should reflect the representations and, more loosely, the mechanisms present in human learners. We present an online learning model of early cross-situational word learning which maps words to objects from context with relatively sparse input. The model operates by rewarding and penalizing probabilities of possible word-to-object mappings based on real-time observation, and using those probabilities to determine a lexicon. We integrate prosodic and gestural cues and allow the learner to evaluate lexical entries. These en- richments allow efficient learning with minimal computational effort, producing results comparable to that of more complex models. Keywords: Cross-situational word learning; online learning Introduction The problem of how children learn the meanings of their first words, a problem for philosophers at least since the time of Augustine, has become an object of scrutiny in psychology and computational cognitive science. On one hand, experi- mental research shows us that young children can use a va- riety of cues (Bloom, 2000) to learn meanings from context with only a few exposures (Carey, 1978); on the other hand, computational modeling work underlines the difficulty of the process, requiring either complex statistical algorithms (Yu and Ballard, 2007; Frank, Goodman, and Tenenbaum, 2009) or large amounts of data (Fazly, Alishahi, and Stevenson, 2008) to achieve adequate learning. The goal of this paper is to simplify the computational problem of early word learn- ing by integrating empirically motivated cues into a simple statistical model that learns object names in real time with speed and precision. Following Yu and Ballard (2007), we integrate prosodic and gestural cues into a statistical learning algorithm for ob- ject names (which comprise the bulk of an early child’s vo- cabulary in many languages, including English). Unlike other models, we give the learner access to a lexicon that adaptively changes as new observations are processed. This allows the learner to check hypothesized word meanings against new input and to enforce a preference for one-to-one mappings between words and objects. These enrichments have roots in experimental research and allow us to construct a simple, effective, and principled online learning model based on re- warding and penalizing probabilities (following Yang, 2002) associated with semantic hypotheses. We believe that this foundation of minimal complexity and empirical motivation produces a more psychologically plausible model. Below we briefly outline some recent experimental and computational work in this area before presenting the details of our model and discussing its advantages. Experimental Work We make use of three principles of early word learning that have emerged from experimental research: (1) mutual exclu- sivity, (2) the availability of gestural and prosodic cues, and (3) the apparent ability of learners to evaluate hypothesized word meanings against new data. Markman (1992) and others have proposed that word learn- ing is guided by a mutual exclusivity assumption, a default as- sumption that objects have only one name. There is indepen- dent experimental evidence (Ichinco, Frank, and Saxe, 2009) that suggests that children disprefer many-to-one word-to- object mappings, and such a preference improves the perfor- mance of a simple learning model, excluding would-be dis- tractors from the semantic hypothesis space when those dis- tractors already have a name in the learner’s lexicon. Mark- man’s view is that mutual exclusivity acts in concert with other default assumptions to extract a finite hypothesis space from Quine’s infamous infinity. Not only must the learner’s hypothesis space be made fi- nite, but it must interact with the learning mechanism in a way that produces quick results. Since Carey (1978) it has been noted that children learn words with impressive speed, often after only a few exposures. To achieve this end, we hold that word learning is guided not only by constraints like mu- tual exclusivity, but also by principles of salience and knowl- edge. This view allows the learning algorithm itself to be quite simple. Under our conception of the process, word learning is guided both by word stress and by gestures, with greater weight being given to semantic hypotheses that map stressed words to gesturally indicated objects. Together we call these two cues “salience cues”, reflecting their function of high-