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+From Resource Grammar to Wide Coverage Translation with GF
+Aarne Ranta
+
+
+GF, Grammatical Framework, was originally designed for the purpose of **multilingual controlled language systems**,
+which would enable high-quality translation on limited domains. The **abstract syntax** of GF defines the semantic
+structures relevant for the domain, and the **concrete syntaxes** map these structures to grammatically correct
+and idiomatic text in each target language. The **reversibility** of GF enables both **generation** and **parsing**,
+and thereby **translation** where the abstract syntax functions as an **interlingua**.
+
+As a bottle-neck of GF applications, it was soon realized that the definition of concrete syntax requires a lot 
+of manual work and linguistic skill, due to the complexities of natural language syntax and morphology. Some of
+the complexities can be ignored in a small system. For instance, in a mathematical system, it may be enough to 
+use verbs in the present tense only. But very much the same linguistic problems must be solved again and again
+in new applications: French verb inflection is much the same in mathematics as in a tourist phrasebook. To solve
+this problem, the **GF Resource Grammar Library** (RGL) was developed, to take care of "low-level" linguistic
+rules such as inflection, agreement, and word order. This enables the authors of **application grammars** to focus
+on the semantics (when designing the abstract syntax) and on selecting RGL functions that produce the idioms they
+want. The RGL grew into an international open-source project, where more than 50 persons have contributed to
+implementing it for 29 languages at the time of writing.
+
+The RGL was thus originally designed to be used just as its name says: as a library
+for application grammars, which were the ones used as **top-level grammars**, i.e. for
+parsing, generation, and translation at run time. Little attention was paid to the usability of RGL as a top-level
+grammar by itself. But when applications accumulated, ranging from technical text to spoken dialogue, the coverage
+of the RGL grew into a coverage that approximates a "complete grammar" of many of the languages. 
+And recently, there has indeed been success in using the RGL as a wide-coverage translation grammar,
+mainly due to Krasimir Angelov's efforts to scale up the size of GF applications from language fragments 
+to open-text processing. This success is a result of four lines of development:
+
+- **More efficient processing**, both due to better algorithms and to an optimized C implementation of a PGF 
+  interpreter, the **C runtime**, achieving speeds competitive with the state of the art, e.g. the Stanford parser.
+  This development is also based on the work of Peter Ljunglöf on GF parsing and Lauri Alanko on the C runtime.
+
+- **Large-scale dictionaries**, both manually built and extracted from free sources, and linked into a multilingual
+  translation dictionary now covering 10k to 60k entries for eight languages. This work was started by Björn Bringert
+  porting the Oxford Advanced Learner's Dictionary for English to GF.
+
+- **Probabilistic disambiguation**, using a model trained from the Penn Treebank. Due to the common abstract syntax,
+  the same model can be readily used for other languages as well, even though the adequacy of this transfer has not
+  been systematically evaluated.
+
+- **Robust parsing**, which recovers from unknown words and syntax by introducing **metavariables** ("question marks")
+  and returning chunk-by-chunk translations; this leads to loss of quality, but fulfills the principle that 
+  "something is better than nothing".
+
+
+The result of this work is indeed a large-coverage translation system, which can be used in the same way as Google
+Translate, Bing, Systran, and Apertium - to "translate anything", albeit with a varying quality. At the moment of
+writing, the performance is not yet generally on the level with the best of the competition, but shows some promising
+improvements in e.g. long-distance agreement and word order. In order to make these into absolute improvements, we
+will need to fix problems that the other systems (or at least some of them) get right but where GF translation 
+often fails:
+
+- **Lexical coverage**, to eliminate parsing failures due to unknown words.
+
+- **Disambiguation**, with more sophisticated than the essentially context-free tree model used now.
+
+- **Speed**, which gets worse with long sentences and with more complex languages.
+
+- **Idiomacy**, due to lack of idiomatic constructions that are not compositional in the RGL but which are
+  often correct in phrase-based SMT.
+
+
+Given that these issues get resolved, the strengths of the GF approach can be made more visible:
+
+- **Grammaticality**, in particular with the already mentioned agreement and word order.
+
+- **Predictability**, in the sense that a local change in the input usually results in just a corresponding
+  local change in the output (unless otherwise required by idiomacy).
+
+- **Feedback**, i.e. the ease of showing the confidence level of the translation, alternative translations,
+  and linguistic information.
+
+- **Adaptability**, i.e. the ease of fixing bugs, adapting the system to special domains, and personalizing it.
+
+- **Multilinguality**, in the sense that once the parsing of the input is settled, the output can be readily
+  rendered into all other languages,
+  and also in the sense that the GF model works equally well for any language pair.
+
+
+The recipes for improvement are, as always, **more work** and **new ideas**. Each of the four weaknesses mentioned
+above can be relieved by more work - in particular, lexical coverage by more work on the lexicon, since
+automatic extraction methods cannot really be trusted. As for disambiguation, new ideas about probabilistic
+tree models are being discussed. As for speed, new ideas on parsing (in particular, the integration of disambiguation
+with parsing) would help, but also the complexity of grammatical structures plays a major role. As for idiomacy,
+more work is being done in introducing **constructions** (non-compositional syntax rules, generalizing the notion of 
+**multiword expressions**, in particular, **phrases** in SMT), but also new ideas are being discussed on how to
+extract such constructions from e.g. phrase tables.
+
+In the following, we will focus on describing the role of grammar in the GF translation system - in particular, how
+RGL can be modified to become usable as a top-level grammar for translating open text.
+As RGL was not meant to be used for parsing open text, but rather for the controlled language generation task,
+it has serious restrictions:
+
+- **Limited coverage**. The RGL does not cover all structures in any language - hence it is likely to fail when
+  parsing unlimited text.
+
+- **Semantic overgeneration**. Semantic distinctions, such as between mass and count nouns, or place and manner
+  adverbials, are assumed to be defined in application grammars; the RGL just defines the combinatorics of
+  elements, but doesn't prescribe which elements can really go together.
+
+- **Spurious ambiguities**. RGL parsing creates more ambiguities than what would be necessary, if there
+  was more semantic control. In addition, there are partly overlapping structures, which generate
+  spurious syntactic ambiguities. 
+  **Example**: the very liberal apposition function.
+
+- **Inefficiency**. Partly because of ambiguities, partly of the deep nesting and complex data structures, parsing
+  with the RGL can be very slow when compared to application grammars, even the comprehensive ResourceDemo grammar.
+  For some languages (Romanian, versions of French and Finnish), parsing is not practically possible at all because   
+  PGF generation fails for memory reasons.
+
+- **Syntax orientation**. The structures of the RGL are rather superficial and don't guarantee translation 
+  equivalence when used as interlingua.
+
+- **Coarse categories**. This is a particular aspect of syntax orientation, and causes at the same time overgeneration
+  and spurious ambiguities. 
+  **Example**: the category ``Adv``.
+
+
+Despite these problems, the RGL has shown to be a possible starting point for large-scale translation. It has a couple
+of advantages speaking for this:
+
+- **Coverage**. Even though not complete, the RGL has grown into a coverage that is close to complete enough; work 
+  with English shows that just about 20% more constructions can take us there.
+
+- **Maintainability**. The RGL is constantly developed and maintained on its own right, and it makes sense to take 
+  advantage of this and avoid duplicated work with some other large-scale grammar.
+
+
+Of course, we are still left with the other 
+option of addressing translation with an //application grammar//, something
+similar to the ResourceDemo with flatter and more semantic structures. But this would in turn require 
+the replication of many rules, even though it would be to a large extent doable by using a **functor**, that is,
+by just one set of rules covering all languages.
+
+Thus the path chosen is a mixture of RGL and application grammar. In brief, the translation grammar consists of
+
+- **Selected RGL modules and functions**, as they are (using restricted inheritance); around 80% of the syntax. 
+
+- **Overridden RGL functions**, with more general types; just a few of them.
+
+- **Overridden RGL linearizations**, typically with more **variants** in individual languages; just a few, but   
+  increasing.
+
+- **Syntax extension**, new categories and functions, around 20% of the syntax, and increasing.
+
+- **Big lexicon**, with an abstract syntax of 65k lemmas, increasing.
+
+- **Constructions**, inspired by (and partly derived from) Construction Grammars, to capture idioms that
+  involve specific lexical items and are therefore "between the syntax and the lexicon".
+
+
+
+
+