Learning to Reason Pragmatically with Cognitive Limitations

Learning to Reason Pragmatically with Cognitive Limitations Adam Vogel Andr´es Gom´ez Emilsson Michael C. Frank acvogel@stanford.edu Stanford Computer Science nc07agom@stanford.edu Stanford Psychology mcfrank@stanford.edu Stanford Psychology Dan Jurafsky Christopher Potts jurafsky@stanford.edu Stanford Linguistics cgpotts@stanford.edu Stanford Linguistics Abstract Recursive Bayesian models of linguistic communication cap- ture a variety of intricate kinds of pragmatic enrichment, but they tend to depend on the unrealistic assumption that agents are invariably optimal reasoners. We present a discrimina- tive model that seeks to capitalize on the insights of such approaches while addressing these concerns about inferential power. The model relies on only approximate representations of language and context, and its recursive properties are lim- ited to the training phase. The resulting behavior is often not optimal, but we present experimental evidence that this subop- timal behavior is closely aligned with human performance on both simple and complex reference games. Introduction Recursive Bayesian models of language production and com- prehension capture a variety of intricate phenomena concern- ing context dependence and pragmatic enrichment (J¨ager, 2007; Franke, 2009; Frank & Goodman, 2012; Bergen et al., 2012; Vogel et al., 2013; Smith et al., 2013). In these models, speakers and listeners reason about each other re- cursively in order to achieve ever more optimal communi- cation systems. These approaches offer precise, algorithmic perspectives on philosophical and linguistic theories of com- munication (Lewis, 1969; Grice, 1975; Horn, 1984), and they make robust predictions about experimental data (Stiller et al., 2011; Rohde et al., 2012; Degen et al., 2013). Despite the success of these models, they raise concerns about inferential power, in that they assume that the agents are invariably optimal reasoners with unbounded computational resources. These concerns can be mitigated by stipulations about depth of iteration (Camerer et al., 2004; Franke, 2009; J¨ager, 2007, 2012), but the models remain computationally demanding and powerful. We seek to capitalize on the insights of these approaches while addressing these concerns. We define a discriminative model of pragmatic reasoning that requires no explicit rep- resentation of the context. Rather, it relies only on features of the environment and language. In addition, the recursive aspects of the model are limited to training: we employ a self-training regime in which, starting with basic models of the speaker and the hearer, we use the speaker to generate su- pervised training data for the listener, and vice versa. Once this phase is complete, the model makes decisions without any recursion. The models are both more efficient and more fallible than the above generative ones. Our model also offers a way to reconcile previous expla- nations of the interpretation or production of pragmatically complex utterances: slowly via complex recursive inferences made as each sentence is processed, (Geurts, 2009; Huang & Snedeker, 2009) or quickly via inferences that are pre- compiled and cached based on previous interactions (Levin- son, 2000; Grodner et al., 2010; Smith et al., 2013). Instead, our discriminatively-trained model instantiates a third possi- bility, extending Jurafsky 2004: learning to directly map sur- face linguistic cues to speaker intent. Like the interpretive models, a learned model explains how context-sensitive infer- ences could be drawn at communication time; like the cached models, it explains why processing could be fast and direct. Our central question is whether our model’s behavior matches human performance across a wide range of situa- tions. To address this, we use collaborative reference games (Rosenberg & Cohen, 1964; Clark & Wilkes-Gibbs, 1986; DeVault et al., 2005) in which a speaker refers to an object in a shared visual scene and the listener uses the speaker’s mes- sage to try to guess the intended referent. By manipulating the properties of the scene and the speaker’s available mes- sages, we can ensure that pragmatic reasoning is required for reliable success. We report several experiments that identify the bounds on human performance in these reference games, and we compare human performance to our model, showing that its inferences closely align with human performance. Reference Games Figure 1 depicts a reference game scenario. There are three potential referents (A, B, and C), each with a pre-specified set of properties (wearing glasses, wearing a hat, having a mus- tache). The speaker is privately assigned one of the referents. Using a message from a pre-defined vocabulary, the speaker tries to convey the identity of this referent to the listener. The listener (19) uses the message to choose one of the referents. Intu- itively, the two participants’ goals are aligned: they win just in case the speaker’s referent is the hearer’s choice. A R1 B R2 C R3 Figure 1: Scenario for a simple reference game. “mustache” Code and data: github.com/acvogel/discriminative-ibr