A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research in natural language dialogue should focus on designing communicative agents which can integrate themselves into these contexts and productively collaborate with humans. In this abstract, we propose a general situated language learning paradigm which is designed to bring about robust language agents able to cooperate productively with humans. This dialogue paradigm is built on a utilitarian definition of language understanding. Language is one of multiple tools which an agent may use to accomplish goals in its environment. We say an agent "understands" language only when it is able to use language productively to accomplish these goals. Under this definition, an agent's communication success reduces to its success on tasks within its environment. This setup contrasts with many conventional natural language tasks, which maximize linguistic objectives derived from static datasets. Such applications often make the mistake of reifying language as an end in itself. The tasks prioritize an isolated measure of linguistic intelligence (often one of linguistic competence, in the sense of Chomsky (1965)), rather than measuring a model's effectiveness in real-world scenarios. 1 Our utilitarian definition is motivated by recent successes in reinforcement learning methods. In a reinforcement learning setting, agents maximize success metrics on real-world tasks, without requiring direct supervision of linguistic behavior. We propose an end-to-end learning environment with multiple language-enabled agents, each with the capacity to define their own internal goals and plans to reach those goals. Each agent may also have different capacities to observe or act in this environment. Their goals are grounded non-linguistic objectives: for example, to reach a desired location, manipulate objects in the environment, or transmit a piece of information. 2 (We address the issue of non-linguistic grounding at length in the next section.) Some fixed-language agents in the environment speak an existing conventional language (e.g. English), and other learning agents are tasked with jointly learning this language while also solving other goals in the environment. The agents are assigned difficult (possibly distinct) tasks which 1 Our motivation here is similar to that of Dagan et al. (2006) , who recognized the negative effects of a community fragmented across different isolated application-specific tasks, and suggested the unified task of recognizing textual entailment (RTE) as a solution. 2 A related line of work in evolutionary linguistics constructs a similar language learning scenario entirely without fixed-language agents (Smith et al., 2003; Steels, 2012; Kirby et al., 2014) . All of the agents in these environments construct a novel language simultaneously to accomplish some shared task. This is an interesting separate line of research, but ultimately a separate task from the understanding and acquisition problems discussed in this abstract.
REF argue that an agent can be considered to have learned a language if it can use it to accomplish goals in its environment.
A Paradigm for Situated and Goal-Driven Language Learning
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Computer Science