Learning in the Rational Speech Acts Model

The Rational Speech Acts (RSA) model treats language use as a recursive process in which probabilistic speaker and listener agents reason about each other’s intentions to enrich the literal semantics of their language along broadly Gricean lines. RSA has been shown to capture many kinds of conversational implicature, but it has been criticized as an unrealistic model of speakers, and it has so far required the manual specification of a semantic lexicon, preventing its use in natural language processing applications that learn lexical knowledge from data. We address these concerns by showing how to define and optimize a trained statistical classifier that uses the intermediate agents of RSA as hidden layers of representation forming a non-linear activation function. This treatment opens up new application domains and new possibilities for learning effectively from data. We validate the model on a referential expression generation task, showing that the best performance is achieved by incorporating features approximating well-established insights about natural language generation into RSA. 1 Pragmatic language use In the Gricean view of language use [18], people are rational agents who are able to communicate efficiently and effectively by reasoning in terms of shared communicative goals, the costs of production, prior expectations, and others’ belief states. The Rational Speech Acts (RSA) model [11] is a recent Bayesian reconstruction of these core Gricean ideas. RSA and its extensions have been shown to capture many kinds of conversational implicature and to closely model psycholinguistic data from children and adults [7, 2, 23, 30, 33]. Both Grice’s theories and RSA have been criticized for predicting that people are more rational than they actually are. These criticisms have been especially forceful in the context of language production. It seems that speakers often fall short: their utterances are longer than they need to be, underinformative, unintentionally ambiguous, obscure, and so forth [1, 10, 16, 24, 28, 29]. RSA can incorporate notions of bounded rationality [4, 13, 20], but it still sharply contrasts with views in the tradition of [6], in which speaker agents rely on heuristics and shortcuts to try to accurately describe the world while managing the cognitive demands of language production. In this paper, we offer a substantially different perspective on RSA by showing how to define it as a trained statistical classifier, which we call learned RSA. At the heart of learned RSA is the back-and-forth reasoning between speakers and listeners that characterizes RSA. However, whereas standard RSA requires a hand-built lexicon, learned RSA infers a lexicon from data. And whereas standard RSA makes predictions according to a fixed calculation, learned RSA seeks to optimize the likelihood of whatever examples it is trained on. Agents trained in this way exhibit the pragmatic behavior characteristic of RSA, but their behavior is governed by their training data and hence is only as rational as that experience supports. To the extent that the speakers who produced the data are pragmatic, learned RSA discovers that; to the extent that their behavior is governed by other factors, learned RSA picks up on that too. We validate the model on the task of attribute selection for referring expression generation with a widely-used corpus of referential descriptions (the TUNA corpus; [34, 15]), showing that it improves on heuristic-driven models and pure RSA by synthesizing the best aspects of both. 1 ar X iv :1 51 0. 06 80 7v 1 [ cs .C L ] 2 3 O ct 2 01 5 Learning in the Rational Speech Acts Model Monroe and Potts r1 r2 r3 (a) Simple reference game. be a rd gl a ss es ti e r1 .5 .5 0 r2 0 .5 .5 r3 0 0 1