Finite-state transducer

A finite-state transducer (FST) is a finite-state machine with two memory "tapes", following the terminology for Turing machines: an input tape and an output tape. This contrasts with an ordinary finite-state automaton, which has a single tape. An FST is a type of finite-state automaton that maps between two sets of symbols. An FST is more general than a finite-state automaton (FSA). An FSA defines a formal language by defining a set of accepted strings, while an FST defines relations between sets of strings.
An FST will read a set of strings on the input tape and generates a set of relations on the output tape. An FST can be thought of as a translator or relater between strings in a set.
In morphological parsing, an example would be inputting a string of letters into the FST, the FST would then output a string of morphemes.
Overview.
An automaton can be said to "recognize" a string if we view the content of its tape as input. In other words, the automaton computes a function that maps strings into the set {0,1}. Alternatively, we can say that an automaton "generates" strings, which means viewing its tape as an output tape. On this view, the automaton generates a formal language, which is a set of strings. The two views of automata are equivalent: the function that the automaton computes is precisely the indicator function of the set of strings it generates. The class of languages generated by finite automata is known as the class of regular languages.
The two tapes of a transducer are typically viewed as an input tape and an output tape. On this view, a transducer is said to "transduce" (i.e., translate) the contents of its input tape to its output tape, by accepting a string on its input tape and generating another string on its output tape. It may do so nondeterministically and it may produce more than one output for each input string. A transducer may also produce no output for a given input string, in which case it is said to "reject" the input. In general, a transducer computes a relation between two formal languages.
Each string-to-string finite-state transducer relates the input alphabet Σ to the output alphabet Γ. Relations "R" on Σ*×Γ* that can be implemented as finite-state transducers are called rational relations. Rational relations that are partial functions, i.e. that relate every input string from Σ* to at most one Γ*, are called rational functions.
Finite-state transducers are often used for phonological and morphological analysis in natural language processing research and applications. Pioneers in this field include Ronald Kaplan, Lauri Karttunen, Martin Kay and Kimmo Koskenniemi.
A common way of using transducers is in a so-called "cascade", where transducers for various operations are combined into a single transducer by repeated application of the composition operator (defined below).
Formal construction.
Formally, a finite transducer "T" is a 6-tuple () such that:
We can view ("Q", "δ") as a labeled directed graph, known as the "transition graph" of "T": the set of vertices is "Q", and formula_2 means that there is a labeled edge going from vertex "q" to vertex "r". We also say that "a" is the "input label" and "b" the "output label" of that edge.
NOTE: This definition of finite transducer is also called "letter transducer" (Roche and Schabes 1997); alternative definitions are possible, but can all be converted into transducers following this one.
Define the "extended transition relation" formula_3 as the smallest set such that:
The extended transition relation is essentially the reflexive transitive closure of the transition graph that has been augmented to take edge labels into account. The elements of formula_3 are known as "paths". The edge labels of a path are obtained by concatenating the edge labels of its constituent transitions in order.
The "behavior" of the transducer "T" is the rational relation ["T"] defined as follows: formula_11 if and only if there exists formula_12 and formula_13 such that formula_14. This is to say that "T" transduces a string formula_15 into a string formula_16 if there exists a path from an initial state to a final state whose input label is "x" and whose output label is "y".
Weighted automata.
Finite State Transducers can be weighted, where each transition is labelled with a weight in addition to the input and output labels. A Weighted Finite State Transducer (WFST) over a set "K" of weights can be defined similarly to an unweighted one as an 8-tuple , where:
In order to make certain operations on WFSTs well-defined, it is convenient to require the set of weights to form a semiring. Two typical semirings used in practice are the log semiring and tropical semiring: nondeterministic automata may be regarded as having weights in the Boolean semiring.
Stochastic FST.
Stochastic FSTs (also known as probabilistic FSTs or statistical FSTs) are presumably a form of weighted FST.
Operations on finite-state transducers.
The following operations defined on finite automata also apply to finite transducers:
Applications.
Context-sensitive rewriting rules of the form "a" → "b" / "c" _ "d", used in linguistics to model phonological rules and sound change, are computationally equivalent to finite-state transducers, provided that application is nonrecursive, i.e. the rule is not allowed to rewrite the same substring twice.
Weighted FSTs found applications in natural language processing, including machine translation, and in machine learning. An implementation for part-of-speech tagging can be found as one component of the OpenGrm library.