An Overview of How Semantics and Corrections Can Help Language Learning

International audienceWe present an overview of the results obtained with a computational model that takes into account semantics and corrections for language learning. This model is constructed with a learner and a teacher who interact in a sequence of shared situations. The model was tested with limited sublanguages of 10 natural languages in a common domain of situations

Experiments using semantics for learning language comprehension and production

Several questions in natural language learning may be addressed by studying formal language learning models. In this work we hope to contribute to a deeper understanding of the role of semantics in language acquisition. We propose a simple formal model of meaning and denotation using finite state transducers, and an algorithm that learns a meaning function from examples consisting of a situation and an utterance denoting something in the situation. We describe the results of testing this algorithm in a domain of geometric shapes and their properties and relations in several natural languages: Arabic, English, Greek, Hebrew, Hindi, Mandarin, Russian, Spanish, and Turkish. In addition, we explore how a learner who has learned to comprehend utterances might go about learning to produce them, and present experimental results for this task. One concrete goal of our formal model is to be able to give an account of interactions in which an adult provides a meaning-preserving and grammatically correct expansion of a child's incomplete utterance

On the Learnability of Shuffle Ideals

PAC learning of unrestricted regular languages is long known to be a difficult problem. The class of shuffle ideals is a very restricted subclass of regular languages, where the shuffle ideal generated by a string u is the collection of all strings containing u as a subsequence. This fundamental language family is of theoretical interest in its own right and provides the building blocks for other important language families. Despite its apparent simplicity, the class of shuffle ideals appears quite difficult to learn. In particular, just as for unrestricted regular languages, the class is not properly PAC learnable in polynomial time if RP 6= NP, and PAC learning the class improperly in polynomial time would imply polynomial time algorithms for certain fundamental problems in cryptography. In the positive direction, we give an efficient algorithm for properly learning shuffle ideals in the statistical query (and therefore also PAC) model under the uniform distribution.T-Party Projec

Polynomial Identification of omega-Automata

We study identification in the limit using polynomial time and data for models of omega-automata. On the negative side we show that non-deterministic omega-automata (of types Buchi, coBuchi, Parity, Rabin, Street, or Muller) cannot be polynomially learned in the limit. On the positive side we show that the omega-language classes IB, IC, IP, IR, IS, and IM, which are defined by deterministic Buchi, coBuchi, Parity, Rabin, Streett, and Muller acceptors that are isomorphic to their right-congruence automata, are identifiable in the limit using polynomial time and data. We give polynomial time inclusion and equivalence algorithms for deterministic Buchi, coBuchi, Parity, Rabin, Streett, and Muller acceptors, which are used to show that the characteristic samples for IB, IC, IP, IR, IS, and IM can be constructed in polynomial time. We also provide polynomial time algorithms to test whether a given deterministic automaton of type X (for X in {B, C, P, R, S, M})is in the class IX (i.e. recognizes a language that has a deterministic automaton that is isomorphic to its right congruence automaton).Comment: This is an extended version of a paper with the same name that appeared in TACAS2

Query learning of derived ω\omega-tree languages in polynomial time

We present the first polynomial time algorithm to learn nontrivial classes of languages of infinite trees. Specifically, our algorithm uses membership and equivalence queries to learn classes of $\omega$-tree languages derived from weak regular $\omega$-word languages in polynomial time. The method is a general polynomial time reduction of learning a class of derived $\omega$-tree languages to learning the underlying class of $\omega$-word languages, for any class of $\omega$-word languages recognized by a deterministic B\"{u}chi acceptor. Our reduction, combined with the polynomial time learning algorithm of Maler and Pnueli [1995] for the class of weak regular $\omega$-word languages yields the main result. We also show that subset queries that return counterexamples can be implemented in polynomial time using subset queries that return no counterexamples for deterministic or non-deterministic finite word acceptors, and deterministic or non-deterministic B\"{u}chi $\omega$-word acceptors. A previous claim of an algorithm to learn regular $\omega$-trees due to Jayasrirani, Begam and Thomas [2008] is unfortunately incorrect, as shown in Angluin [2016]

Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries

A Büchi automaton is strongly unambiguous if every word w ∈ Σ^ω has at most one final path. Many properties of strongly unambiguous Büchi automata (SUBAs) are known. They are fully expressive: every regular ω-language can be represented by a SUBA. Equivalence and containment of SUBAs can be decided in polynomial time. SUBAs may be exponentially smaller than deterministic Muller automata and may be exponentially bigger than deterministic Büchi automata. In this work we show that SUBAs can be learned in polynomial time using membership and certain non-proper equivalence queries, which implies that they are polynomially predictable with membership queries. In contrast, under plausible cryptographic assumptions, non-deterministic Büchi automata are not polynomially predictable with membership queries