Pragmatics is central to natural language, enabling speakers to communicate efficiently by using structures like ellipsis and anaphora to shorten utterances without loss of meaning. These structures require a listener to interpret an ambiguous form - like a pronoun - and infer the speaker’s intended meaning—who that pronoun refers to. Despite this potential to introduce ambiguity, anaphoric structures are ubiquitous across human language. In an effort to better understand how such structures emerge, we look to see if analogous structures can emerge in languages used between artificial neural networks that are trained to solve a communicative task. We show the following: first, despite the potential for increased ambiguity, languages with anaphoric structure are highly learnable by neural models. Second, anaphoric structure emerges naturally without needing to impose additional constraints on the networks. Finally, we introduce an explicit efficiency pressure on the speaker and show that this increases the prevalence of anaphoric structures. We conclude that certain pragmatic structures straightforwardly emerge between neural networks without explicit pressures, but that the balance between speaker and listener needs conditions the degree and nature of their emergence.

Anaphoric Structure Emerges Between Neural Networks

Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts. This property allows humans to create and interpret novel sentences, generalizing robustly outside their prior experience. Neural networks have been shown to struggle with this kind of generalization, in particular performing poorly on tasks designed to assess compositional generalization (i.e. where training and testing distributions differ in ways that would be trivial for a compositional strategy to resolve).
Their poor performance on these tasks may in part be due to the nature of supervised learning which assumes training and testing data to be drawn from the same distribution. We implement a meta-learning augmented version of supervised learning whose objective directly optimizes for out-of-distribution generalization. We construct pairs of tasks for meta-learning by sub-sampling existing training data. Each pair of tasks is constructed to contain relevant examples, as determined by a similarity metric, in an effort to inhibit models from memorizing their input. Experimental results on the COGS and SCAN datasets show that our similarity-driven meta-learning can improve generalization performance.

Meta-Learning to Compositionally Generalize

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9D: Semantics: Sentence-level Semantics, Textual Inference and Other areas #3

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IJCNLP-AACL 2021

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Anaphoric Structure Emerges Between Neural Networks

Meta-Learning to Compositionally Generalize

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