Deep Grammarsin Hybrid Machine Translation

University of Bergen

Helge Dyvik

Lexicon, Lexical Semantics, Grammar, and Translation for Norwegian

A 4-year project (2002 - 2006) involving groups at:•The University of Oslo•The University of Bergen•NTNU (The University of Trondheim)

Cooperation with PARC (John Maxwell) and others

The LOGON systemSchematic architecture

XLE: Xerox Linguistic EnvironmentA platform developed over more than 20 years

at Xerox PARC (now PARC)Developer: John Maxwell

•LFG grammar development•Parsing•Generation•Transfer•Stochastic parse selection•Interaction with shallow methods

An LFG analysis:

Det regnet'It rained'

•Develops parallel grammars on XLE:English, French, German, Norwegian, Japanese, Urdu, Welsh, Malagasy, Arabic, Hungarian, Chinese, Vietnamese•‘Parallel grammars’ means parallel f-structures:

A common inventory of featuresCommon principles of analysis

ParGram: The Parallel Grammar ProjectA long-term project (1993-)

LOGON Analysis Modules

Input string

•Tokenization•Named ent.•Compounds•Morphology

LFG lexicons:•NKL-derived•Hand coded

Lexicaltemplates

SyntacticrulesRule templates

c-structures

f-structures

MRSs

Norsk ordbanklexicon

XLE Parser

NorGram String of stemsand tags

Output-inputSupporting knowledgebase

Scope of NorGram

Lexicon: about 80 000 lemmas.In addition:

Automatically analyzed compoundsAutomatically recognized proper names"Guessed" nouns

Syntax: 229 complex rules, giving rise to about 48 000 arcs

Semantics: Minimal Recursion Semantics projections for all readings

Coverage

Performance on an unknown corpus of newspaper text:

•17 randomly selected pieces of text, limited to coherent text,

•comprising 1000 sentences

•taken from 9 newspapers

Adresseavisen, Aftenposten, Aftenposten nett, Bergens Tidende,

Dagbladet, Dagens Næringsliv, Dagsavisen, Fædrelandsvennen, Nordlys,

•from the editions on November 11th 2005.

The LOGON challenge:

From a resource grammar based on independent linguistic principles, derive MRS structures harmonized with the MRS structures of the HPSG English Resource Grammar.

Semantics for translation:Two issues

• The representational subset problem- Desirable: normalization to flat structures withunordered elements.

• Complete and detailed semantic analyses may be unnecessary.

- Desirable: rich possibilities of underspecification

Basics of

Minimal Recursion Semantics

•Developers: A. Copestake, D. Flickinger, R. Malouf, S. Rieheman, I.

Sag

•A framework for the representation of semantic information

•Developed in the context of HPSG and machine translation

(Verbmobil)

•Sources of inspiration:

- Quasi-Logical Form (H. Alshawi):

underspecification, e.g. of quantifier scope

- Shake-and-bake translation (P. Whitelock):

a bag of words as interface structure

An MRS representation

• is a bag of semantic entities (some corresponding to words,

some not),

each with a handle,

• plus a bag of handle constraints allowing the underspecification

of

scope,

• plus a handle and an index.

• Each semantic entity is referred to as an Elementary Predication

(EP).

• Relations among EPs are captured by means of shared

variables.

• There are three elementary variable types:

- handles (or 'labels') (h)

- events (e)

- referential indices (x)

From standard logical form to MRS

«Every ferry crosses some fjord»

Two readings:

Replace operators with generalized quantifiers:

every(variable, restriction, body)some(variable, restriction, body)

The first reading (wide-scope every):

var restriction body

Make the structure flat:• give each EP a handle• replace embedded EPs by their handles• collect all EPs on the same level (understood as conjunction)

Underspecified scope by means of handle constraints:

Make the structure flat:• give each EP a handle• replace embedded EPs by their handles• collect all EPs on the same level (understood as conjunction)

Wide scope: someWide scope: every

MRS as feature structure (also adding event variables):

Norwegian translation: «Hver ferge krysser en fjord»

Projecting MRS representationsfrom f-structures

«Katten sover»'The cat sleeps'

Projecting MRS representationsfrom f-structures

«Katten sover»'The cat sleeps'

mrs::

mrs::

mrs::

Composition: Top-level MRSwith unions of HCONS and RELS:

Post-processing this structurebrings us back to the LOGON MRS format:

http://decentius.aksis.uib.no/logon/xle-mrs.xml

Examples

bil 'car' (as in "Han kjøpte bil" 'He bought [a] car')

No SPEC

disse hans mange spørsmål 'these his many questions'

Multiple SPECs

Han jaget barnet ut nakent'He chased the child out naked'

The Transfer Component

Developer of the formalism: Stephan Oepen

Example of transfer

Source sentence:

Henter han bilen sin?fetches he car.DEF POSS.REFL.SG.MASC'Does he fetch his car?'

Alternative reading:'Does he fetch the one of the car?'

Parse output:

Choosing the first reading of Henter han bilen sin?

Choosing the first reading of Henter han bilen sin?

The variables have features.Interrogative is coded as [SF ques] on the event variable.

Two of fourtransferoutputs

Norwegiantransferinput

One of fourEnglishtransferoutputs

Generator output from the chosen transfer output

Transfer formalism(Stephan Oepen)

The form of a transfer rule:

C = contextI = inputF = filterO = output

Simple example:Lexical transfer rule, transferring bekk into creek

No context, no filter, only the predicate is replaced.

Example with a context restriction:gå en tur (lit. 'go a trip') is transferred into the light-verb constructiontake a trip.

In the context of _tur_n as its second argument,_gå_v is transferred to _take_v.

The SEM-I(Semantic Interface)

A documentation of the external semantic interfacefor a grammar, crucial for the writer of transfer rules.

In order to enforce the maintaining of a SEM-I,LOGON parsing returns fail if every parse containsat least one predicate not in the SEM-I.

A small sectionof the verb partof the NorGramSEM-ISize of the NorwegianSEM-I: slightly lessthan 6000 entries

Parse Selection

Parsing, transfer and generation may each givemany solutions, leading to a fanout tree:

The outputs at each of the three stages arestatistically ranked.

Example of a four-way ambiguity:

Det regnet 'It rained'/'It calculated'/'That one calculated'/'That rain'

The ParsebankerEfficient treebank building by discriminants

Developer: Paul Meurer, Bergen

Predecessors in discriminant analysis:David Carter (1997)Stephan Oepen, Dan Flickinger & al. (2003)

1

2

3

4

Packed representations and discriminants(Paul Meurer)

Clicking on one discriminant is in this case sufficientto select a unique solution:

The Parsebanker

'After all, a human being must be something more than a machine?'

TigerSearchThe implementation is under development by Paul Meurer

Find selected prepositional phrases with sentential objects:

Find selected prepositional phrases with the preposition 'om' and nominal objects:

Find topicalized objects: