An Incremental Iterated Response Model of Pragmatics

Recent Iterated Response (IR) models of pragmatics conceptualize language use as a recursive process in which agents reason about each other to increase communicative efficiency. These models are generally defined over complete utterances. However, there is substantial evidence that pragmatic reasoning takes place incrementally during production and comprehension. We address this with an incremental IR model. We compare the incremental and global versions using computational simulations, and we assess the incremental model against existing experimental data and in the TUNA corpus for referring expression generation, showing that the model can capture phenomena out of reach of global versions

Learning to refer informatively by amortizing pragmatic reasoning

A hallmark of human language is the ability to effectively and efficiently convey contextually relevant information. One theory for how humans reason about language is presented in the Rational Speech Acts (RSA) framework, which captures pragmatic phenomena via a process of recursive social reasoning (Goodman & Frank, 2016). However, RSA represents ideal reasoning in an unconstrained setting. We explore the idea that speakers might learn to amortize the cost of RSA computation over time by directly optimizing for successful communication with an internal listener model. In simulations with grounded neural speakers and listeners across two communication game datasets representing synthetic and human-generated data, we find that our amortized model is able to quickly generate language that is effective and concise across a range of contexts, without the need for explicit pragmatic reasoning.Comment: Accepted to CogSci 202

Perceptual difficulty differences predict asymmetry in redundant modification with color and material adjectives

When referring to objects, speakers are often more specific than necessary for the purpose of establishing unique reference, e.g., by producing redundant modifiers. A computational model of referring expression production that accounts for many of the key patterns in redundant adjectival modification assumes that adjectives differ in how noisy (reliable), and consequently, how useful they are for reference. Here we investigate one hypothesis about the source of the assumed adjectival noise: that it reflects the perceptual difficulty of establishing whether the property denoted by the adjective holds of the contextually relevant objects. In Exp.1, we collect perceptual difficulty norms for items that vary in color and material. In Exp. 2, we test the highest (material) and lowest (color) perceptual difficulty items in a reference game and find that material is indeed less likely to be mentioned redundantly, replicating previous work. In Exp. 3, we obtain norms for the tested items in a second perceptual difficulty measure with the aim of testing the effect of perceptual difficulty within property type. The overall results provide preliminary support for the hypothesis that the propensity to redundantly use color over material adjectives may be driven by the relative ease of assessing an object’s color, compared to the relative difficulty of assessing its material

Reinforced Natural Language Interfaces via Entropy Decomposition

In this paper, we study the technical problem of developing conversational agents that can quickly adapt to unseen tasks, learn task-specific communication tactics, and help listeners finish complex, temporally extended tasks. We find that the uncertainty of language learning can be decomposed to an entropy term and a mutual information term, corresponding to the structural and functional aspect of language, respectively. Combined with reinforcement learning, our method automatically requests human samples for training when adapting to new tasks and learns communication protocols that are succinct and helpful for task completion. Human and simulation test results on a referential game and a 3D navigation game prove the effectiveness of the proposed method

The Usability of Pragmatic Communication in Regular Expression Synthesis

Programming-by-example (PBE) systems aim to alleviate the burden of programming. However, user-specified examples are often ambiguous, leaving multiple programs to satisfy the specification. Consequently, in most prior work, users have had to provide additional examples, particularly negative ones, to further constrain the search over compatible programs. Recent work resolves additional ambiguity by modeling program synthesis tasks as pragmatic communication, showing promising results on a graphics domain using a rudimentary user-study. We adapt pragmatic reasoning to a sub-domain of regular expressions and rigorously study its usability as a means of communication both with and without the ability to provide negative examples. Our user study (N=30) demonstrates that, with a pragmatic synthesizer, end-users can more successfully communicate a target regex using positive examples alone (95%) compared to using a non-pragmatic synthesizer (51%). Further, users can communicate more efficiently (57% fewer examples) with a pragmatic synthesizer compared to a non-pragmatic one