What is NLG? Input • Formal representation of some information (linguistic or non-linguistic) Output • Single sentences or texts (reports, explanations, instructions, etc.) Resources drawn upon • Context of situation • World and domain knowledge • Domain communication knowledge • Linguistic knowledge
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1
What is NLG?
Input• Formal representation of some information (linguistic or
non-linguistic)
Output• Single sentences or texts (reports, explanations,
instructions, etc.)
Resources drawn upon• Context of situation• World and domain knowledge• Domain communication knowledge• Linguistic knowledge
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NLG in Text Summarization
On Monday, GreenChip Solutions made an acquisition offer to BuyOut Inc., a St. Louis-based plastic tree manufacturer that had tremendous success in equipping American households with pink plastic oak trees.
Tom poured coolant into the radiator. Tom schüttete Kühlmittel in den Kühler.
CAUSER
OBJECT
PATH DESTINATION
DIRECTION
32 Lexicalization Strategies (Equating source and lexical entities)
Animal
Water Animal
Fish
Shark
Mammal
Cetacean
Dangerous Fish
Sand Shark
Dolphin
Tiger Shark
FN (Reiter)
33 Lexicalization Strategies (Indexing)
`lecture´ => LECTURE
• Information available in the lexicon:
TALK, PRESENTATION, ...
[to] lecture
give [ART ~]
deliver [ART ~]
attend [ART ~]
follow [ART ~]
prepare [ART ~]
...
• Also (possibly) available: Paraphrasing rules
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Aggregation
Aggregation is the process of entity grouping at various levels of processing with the goal to avoid redundancy.
Types of aggregation:
• Conceptual aggregation
• Lexical aggregation
• Syntactic aggregation
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Aggregation (Some Examples, 1)
Conceptual aggregation:1. Heavy rain is expected in Zuffenhausen.
2. Heavy rain is expected in Cannstatt3. Heavy rain is expected in Vaihingen
1.-3. Heavy rain is expected in Metropolitan Stuttgart.
Lexical aggregation:1. From 9 am to 11 am the ozone concentration fell.2. Then the ozone concentration rose.3. Then the ozone concentration fell.4. Then the ozone concentration rose
1.-4. From 9 pm on the ozone concentration varied.
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Aggregation (Some Examples, 2)
Syntactic aggregation:
• Referential aggregation
1. The employment rate among women fell.
2. The employment rate among men rose.
1.+2. The employment rate among women fell while that among men rose.
• Elision
1. The employment rate among women rose.
2. The employment rate among men rose.
1.+2. The employment rate rose.
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Aggregation, Rule Examples
(x / process
:agent ?A
...)
AND(x / process
:agent ?B
...)
(x / process
:agent (c /conj:arg (?A ?
B))...)
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Choice of Referring Expressions
The process of determining how to identify entities known from the extralinguistic context and entities introduced in the previous discourse.
Types of referring expressions:
• Noun Definiteness/Deixis
• Pronominalization
• Elision
• Direct lexical references
• Indirect lexical references
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Referring Expressions Examples
1. John saw a small boy. The boy was crying.
2. John saw a small boy. He was crying ...3. John saw a small boy. The poor kid was crying
4. The comments are not restricted to classic AI, but are appropriately applied to theoretical linguistics as well.
5. Today‘s lecture is on Agent Technology. The lecturer is a visiting professor from the UCLA.
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Referring Expressions , Rule Example
IF (X is denotation of a transformation
ANDProp.focus mentioned in last sentence
ANDResultative Noun (RN) available for X)
THEN IF (RN unique)THEN CHOOSE RN
ELSE ...Put the batter into the oven. Remove the cake in two hours.
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Syntacticization (1)
Syntacticization is the process of choosing the most appropriate syntactic construction for a message.
Options to be chosen from:
• Sequence of sentences vs. Coordination vs. Subordination:
The Black Forest station is located in the woods. At this station, an ozone concentration of 259 g/m3 has been measured.
vs.
At the Black Forest station, which is located in the woods, an ozone concentration of 259 g/m3 has been measured.
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Syntacticization (2)
• Sentence vs. Nominal Phrase:
Tomorrow, it is cloudy with sunny periods and patchy drizzle ending in the afternoon.
vs.
Tomorrow, clouds with sunny periods and patchy drizzle till the afternoon.
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Interdependency in Microplanning
Problematic:
• Nearly all microplanning tasks are intertwined with each other, i.e., the realization of one depends on the realization of the other and vice versa.
• Theoretically still unclear which phenomenon belongs to which task.
• Theoretically still not entirely clear whether to treat microplanning as a set of different tasks.
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Interdependency in Microplanning
1. Today‘s lecture is on Agent Technology. The lecturer is a visiting professor from the UCLA.
2. The topic of today‘s lecture is Agent Technology. It is given by a visiting professor from the UCLA.
3. A visiting professor from the UCLA gives today a lecture on Agent Technology.
4. Today‘s lecture, which is on Agent Technology, is given by a visiting professor from the UCLA.
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Surface realization (1)
Goal:
To realize a sentence/phrase plan as a sentence/phrase at the surface
Depending on the scale of variation and complexity of texts required, several generation techniques are available:
• Canned text
• Templates
• Full fledged generation
• Combination of the above techniques
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Templates, Examples
1. Cloudy with sunny periods in <location>. The temperature is expected to rise to <number> degrees C.
2. In <location>, the ozone concentration reached <number> µg/m3.
3. <user-name> was logged in for <duration> hours.
4. The unemployment rate among men for the month of <month> <decreased/increased/remained stable>.
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Full fledged generation
...
subst. time value
SO2
NO2
19.11.99:18:00
... ... ...
9
7819.11.99:18:00
dimen.
µg/m3
...
station
Berlin
(measure
station: Berlin,
substance:SO2,
time: 19.11.99:18:00,
value:200,
dimension: µg/m3)
On 19.11. at 6pm the SO2 concentration reached 200 µg/m3 in Berlin.
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Technique of text production
• A paragraph or sentence never changes – its appearance being triggered by specific input data or being obligatory.
Canned Text is appropriate • Only a few variations of sentence and/or phrase structures
are available to communicate a specific information; within a sentence/phrase structure a few arguments may change.
Templates are appropriate• The information to be communicated may vary and the
sentence structures that express depend on the discourse structure progression
Full fledged generation is appropriate
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Multimodal Generation
Text presentations and graphical presentations have differing strengths and weaknesses. Their combination can achieve powerful synergies.
However, simply placing textual and graphical information together is no guarantee that one view is supportive of another.If the perspective on the data taken in a graphic and that taken in a text have no relation, then the result is incoherence rather than synergy.
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Multimodal Generation: Coherence
Multimodal generation is a goal-directed activity, i.e.,
when generating a multimodal document
• the author pursues certain comm. goals
Intentional Structure of the document
• the author chooses an organization of the information that supports its comm. goals
Discourse Structure of the document
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Intentional Structure of a Document
The Intentional Structure of a document is a hierarchy of Acts that ensure that the goal(s) is/are achieved
1. At each level of the hierarchy, at least one main act must be specified
2. A main act may be supported by subsidiary acts
3. The system must keep track of the beliefs it has and the facts it knows about
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Discourse Structure of a Document
The Discourse Structure of a document is a hierarchy of coherence relations – as, e.g., specified in RST.
Examples of RST-Relations:
ContrastElaborationMotivationEnablementBackground
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RST-relation example (1)
1. Heavy rain and thunderstorms in North Spain and on the Balearic Islands.
2. In other parts of Spain, still hot, dry weather with temperatures up to 35 degrees Celcius.
CONTRAST
Symmetric (multiple nuclei) Relation:
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RST-relation example (2)
2. In Cadiz, the thermometer might rise as high as 40 degrees.
1. In other parts of Spain, still hot, dry weather with temperatures up to 35 degrees Celcius.
ELABORATION
Asymmetric (nucleus-satellite) Relation:
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Cohesive Links between Doc. Elements
multimodal referring expressions
vs.
crossmode referring expressions
A multimodal referring expression refers to a world object via a combination of at least two media. Each mode conveys some discriminating attributes of the object.
A crossmode referring expression refers to a document part in a different presentation mode.
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Planning the content and the structure
Communicative Structure + Discourse Structure
Textplanning in (monomodal) Text Generation
Planning mechanisms for multimodal documents can and should be derived from the text planning
mechanisms
!!!RST-like Text Planning!!!
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RST-based Content Determination (2)
• Model of mental states and communicative goals, e.g.:
– (know ?agent (ref ?description))
– (bel ?agent (?predicate ?e1 ?e2))
• Example: plan operator for MOTIVATION from Moore/Paris:
– EFFECT: (MOTIVATION ?act ?goal)
– CONSTRAINTS: (AND (STEP ?act ?goal)
– (GOAL ?hearer ?goal))
– NUCLEUS: (BEL ?hearer (STEP ?act ?goal))
– SATELLITES: NIL
• Moore/Paris text planner works by top-down hierarchical
expansion; alternative: bottom-up planning, e.g. (Marcu 1997)
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WIP Planning Strategies
Introduce an object by showing a picture of it:
Header: (Introduce System User ?object Graphics)
Effect: (BMB System user (Isa ?object ?concept)
Applicability Conditions:
(Bel System (Isa ?object ?concept)
Main Acts:
(S-Depict System User ?object ?pic-obj ?picture)
Subsidiary Acts:
(Label System User ?object ?medium)
(Provide-Background System User ?object ?pic-obj ?picture Gr..)
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WIP Planning Strategies
Provide Background:
Header: (Provide-Background System User ?x ?px ?picture Graphics)
Effect: (BMB System user (Encodes ?px ?x ?picture)
Applicability Conditions:
(And (Bel System (Encodes ?px ?x ?picture))
(Bel System (Perceptually-Access-p User ?x))
(Bel System (Part-of ?x ?z))
Main Acts:
(S-Depict System User ?z ?pz ?picture)
Subsidiary Acts:
(Achieve System (BMB System User (Encodes ?pz ?z ?picture
?medium)
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WIP Planning Strategies
Establish a coreferential link:
Header: (Establish-coref System User ?r1 ?r2 Graphics)
Effect: (BMB System user (Coref ?r1 ?r2)
Applicability Conditions:
(And (BMB System User (Encodes ?spec1 ?r1))
(BMB System User (Text-Obj ?spec1 ?r1))
(BMB System User (Encodes ?spec2 ?r2))
(BMB System User (Pic-Obj ?spec2 ?r2)))
Main Acts:
(S-Annotate System User ?spec1 ?spec2 ?picture)
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WIP Planner
1. The user posts the goal to be achieved.
2. The planner identifies the potentially applicable strategies by searching the strategy library for all strategies whose effect field matches the goal.
3. For each strategy found, the conditions are checked.
4. Select one of the applicable strategies (e.g., depending on the preference given to a specific mode).
5. Place the strategy in the corresponding plan node
6. If the strategy has subsidiary act strategies, expand the first; otherwise go to the nearest non-expanded strategy