Summary Cheat Sheet - Natural Language Generation (INFOMNLG)
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Course
Natural Language Generation (INFOMNLG)
Institution
Universiteit Utrecht (UU)
This cheat sheet contains the most important information from the course and refers to pages in the full summary for extra details. Everything is clearly highlighted.
Cheat Sheet NLG
Strategic choices: what to say (based on input, knowledge, target language)
Tactical choices: how to say it (dependent on language)
Classic pipeline for NLG and its subtasks (p3)
Modular architecture breaks down the main task into sub-tasks, modelling each one separately.
Dominant and classical approach.
In end-to-end models: no/fewer explicit subtasks.
Raw, unstructured data before document planning, add (1) signal analysis - extract patterns
and trends – and (2) data interpretation
“Classic” BabyTalk system(p5)
Classic systems vs Contemporary models (p7): tension between control and fluency
NLG subtasks in more detail – image to caption (p7)
NLG as (cognitive) modelling of language (p10)
Production errors
Syntax errors spreading activation; human memory is associative
Levelt’s “blueprint” for the speaker (p11)
Modularity (p16) and incrementality (p17)
Relationship between blueprint and classic NLG pipeline (p15)
Conceptual preparation and role of self-perception
How do people identify objects using language? (p17)
Referring expression is contextually available
REG more intertwined approach to what to say + how to say it
1
, REG algorithms and the Gricean maxims of conversation (p18)
Referential form depends on salience of discourse entities and context; salience depends on
o Centering theory (syntactic role)
o Accessibility theory (accessibility/ availability of entity; more = shorter form)
Older models were deterministic, ML models for human variation
Are REG algorithms cognitively plausible? (p19)
Cooperative principle; People behave rationally
Default expectations not fulfilled implicatures (hidden meaning, not explicitly stated)
Conversational maxims: Quantity, Quality, Relation, Manner
Choosing the content of definite descriptions (p20)
Greedy algorithm: most discriminatory property
Incremental algorithm: what a speaker would be likely to select, using preference order
o + efficient, psycho plausible, accounts for overspecification
o – deterministic; people are not (PRO: use sth fully discriminatory, else preference)
High scene variation high eagerness to over specify
Alignment of data and text to train NLG systems (p25)
Source pairs from web = loosely aligned
Automatic alignment = more tightly aligned; noisy
Crowdsource = tighter aligned; expensive, smaller datasets
Opportunistic data collection favours better represented languages
Data-driven content selection: Learning statistical models to decide what to say ( p26)
Content selection as classification problem, but: facts have dependencies & poor coherence
Collective content selection: consider individual preference + probability of linked facts
optimisation
Using Language Models to decide how to say it (p28)
N-gram models look at limited no words before predicting next word
Markov models only look at immediate past state (previous word as only context)
Long-distance dependencies: challenge for classical LMs
Overgenerate-and-rank + capture variation & handles probabilistic linguistic rules -
ambiguity
o HALOGEN Input: recursive, order-independent, contains grammatical and/or
semantic elements. Recasting helps convert between different representations within it
o HALogen Base Generator rules: recasting, ordering, filling, morphing
o Output: forest of trees represents all possible realisations, ranked using a pretrained LM
Rational Speech Act (RSA) model (p34)
Cooperative language use; utility-based reasoning
Pragmatic inference; iterative process
Pragmatic speaker chooses utterance based on expected utility utility = surprisal – cost
(speakers’ effort to avoid ambiguity)
Utility-based reasoning: informative but not overly verbose (= greedy algorithm)
Combining computer vision and NLG: Reference in the ReferIt Game ( p38)
Model calculates correct colour for an obj in a scene by analysing colour histograms
Short introduction to Feedforward neural networks (p41)
Type of NN that accepts a fixed-size input and compute a predicted value
2
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