Bernardo Magnini, presidente della Associazione Italiana di Linguistica Computazionale, in questa relazione fa il punto della ricerca nel campo del Natural Language Processing e del Machine Learning per quanto riguarda lo sviluppo di tecnologia per chat intelligenti. Circoscriverà l’argomento a brevi conversazioni via tastiera nell’ambito delle interazioni tra cliente e call centre, a supporto di operatori. La tecnologia attuale, tra l’altro, comprende metodi di classificazione automatica delle richieste, l’applicazione di tecniche di similarità semantica per il recupero automatico di FAQ, l’interpretazione semantica delle richieste con tecniche di Question/Answering per ottenere risposte da dati strutturati, e l’organizzazione di grandi flussi di richieste utilizzando grafi semantici a supporto di processi inferenziali. Infine, i recenti sviluppi nel campo delle reti neurali aprono nuove prospettive e sfide per la gestione automatica di dialoghi complessi. Bernardo Magnini e’ ricercatore senior presso la Fondazione Bruno Kessler di Trento, dove è coordinatore scientifico della Linea di Ricerca Cognitive Computing. I suoi interessi di ricerca sono nel campo del trattamento automatico del linguaggio, in particolare della semantica computazionale, settore in cui ha coordinato vari progetti internazionali. E’ tra i fondatori, e attuale Presidente, della Associazione Italiana di Linguistica Computazionale. http://hlt-nlp.fbk.eu/people/profile/magnini

1. Verso la chat “intelligente”
Bernardo Magnini
Fondazione Bruno Kessler, Trento
magnini@fbk.eu

2. Outline
• Natural Language Processing
• The role of Machine Learning
• Scenario 1: question answering over structured data
• The role of knowledge modeling
• Scenario 2: FAQ retrieval
• The role of text-to-text technologies
• Toward the intelligent chat
• Learning complex dialogues

3. Pipeline of NLP tools
• HTML cleaner
• Tokenizer and Sentence Splitter
• POS tagging
• Morphological analysis
• Chunking and parsing
• Named entities
• Temporal expressions
• Key concepts
• Geo-coding
Ongoing
• Sentiment analysis
• NER for German
Semantic Annotations
TextPro is freely distributed for research purposes
http://textpro.fbk.eu/

4. A Pipeline of NLP taggers
CleanPro TokenPro SentencePro MorphoPro
EntityPro
LemmaPro
Geo
Coder
Syntax
Pro
TagPro
KX ChunkPro
TimePro
TextPro: - a cascade of “annotators”
- Input: Pure text (UTF-8)
- Output: tabularformat (IOB annotation),XML

5. Output of an NLP Pipeline
token tokenid tokenstart tokenend pos lemma chunk entity timex
US 1 0 2 NP0 __NULL__ B-NP B-GPE O
President 2 3 12 NP0 president I-NP O O
Barack 3 13 19 NP0 __NULL__ I-NP B-PER O
Obama 4 20 25 NP0 __NULL__ I-NP I-PER O
delayed 5 26 33 VVD delay B-VP O O
a 6 34 35 AT0 a B-NP O O
decision 7 36 44 NN1 decision I-NP O O
on 8 45 47 PRP on B-PP O O
potential 9 48 57 NN1 potential B-NP O O
military 10 58 66 AJ0 military I-NP O O
action 11 67 73 NN1 action I-PP O O
against 12 74 81 PRP against B-PP O O
Syria 13 82 87 NP0 syria B-NP B-LOC O
after 14 88 93 CJS after B-PP O O
September 15 94 103 NP0 september B-NP O B-DATE
9 16 104 105 CRD __NULL__ I-NP O I-DATE
Provided by: TokenPro TagPro LemmaPro ChunkPro EntityPro TimePro

6. Graph-based Representation
Abstract Meaning Representation (AMR) format
The AMR above can be expressed variously in English:
The boy wants the girl to believe him.
The boy wants to be believed by the girl.
The boy has a desire to be believed by the girl.
The boy’s desire is for the girl to believe him.
The boy is desirous of the girl believing him.

7. United States presidential election of 2008, scheduled for Tuesday November 4, 2008, will be the 56th consecutive quadernnial United States
presidential election and will select the President and the Vice President of the United States. The Republican Party has chosen JohnMcCain, the
senior United States Senator from Arizona as its nominee; the DemocraticParty has chosen Barak Obama, the junior United States Senator from
Illinois, as its nominee.
United_B-LOC States_I-LOC presidential_O election_O of_O 2008_O ,_O scheduled_O for_O Tuesday_O November_O 4_O ,_O2008_O ,_Owill_O be_O
the_O 56th_O consecutive_O quadernnial_O United_B-LOC States_I-LOC presidential_O election_O and_O will_O select_O the_O President_B-PER
and_O the_O Vice_B-PER President_I-PER of_I-PER the_I-PER United_I-PER States_I-PER. The_O Republican_B-ORG Party_I-ORG has_O chosen_O
John_B-PER McCain_I-PER ,_O the_O senior_O United_B-PER States_I-PER Senator_I-PER from_O Arizona_B-LOC as_O its_O
United,null,null,null,States,presidential,election,1,1,1,1,0,1,0,0,1,1,1,1,1,0,1,0,1,1,ed,ted,Uni,UnNamed Entity
Classification
supervised
TEST DATA
answer
Learning Algorithm Trained Machine
TRAIN DATA
Machine Learning Development Cycle
• Typical development process: task definition, dataset (training, test), feature
extraction, evaluation
• Advantages: low cost, good performance
• Drawbacks: domain portability, poor control on results

8. Active Learning
• A technique for selecting training
examples with high probability to
change wrong classifications
• Active learning selection vs random
selection
• Involve the user in the learning
process
• Improving performance correcting
the system errors
• Significantly reduces development
time

9. hasInfrastructur
e
hasName
Cinema
hasNam
e
hasEve
nt
Event
isinSite
PostalAddress
Contact
hasPostalAddre
ss
hasContac
t
Director
Astra
Destination
isInDestinati
on
Movie
hasEventConten
t
hasDirector
hasName
194 mins
Titanic
Cameron
duration
Movie
hasEventConten
t
hasDirecto
r hasName
164 mins
E.T.
Spielberg
duration
Director
hasName
• Task: given a question find a
precise answer contained in
database/knowledge base
• Need modeling domain
knowledge: the ontology
• E.g. An ontology for cultural
events (movies, sport event,
etc.)
Scenario 1: Question/Answering on Structured Data

10. Which cinema is showing Titanic by Cameron in Trento?
Scenario 1: Question/Answering on Structured Data
10Madrid, June 1, 2010- Bernardo Magnini
Titanic is showing today in Trento at Cinema Astra at 8 p.m.
Price of the ticket is 10 Euros.

11. Which cinema is showing Titanic by Cameron in Trento?
EAT CONSTRAINT CONTEXT
?CINEMA:X
Movie-hasCinema(“Movie:Titanic” Cinema:X)
Movie-director(“Titanic” “Cameron”)
Cinema-loc(Cinema:x Trento)
Context-Time(Q, December 14th)
Context-LOC(Q, Trento)
11
Scenario 1: Question/Answering on Structured Data

12. 12
Which cinema is showing Titanic by Cameron in Trento?
EAT CONSTRAINT CONTEXT
?CINEMA:X
Movie-hasCinema(“Titanic” Cinema:X)
Movie-director(“Titanic” “Cameron”)
Cinema-loc(Cinema:x Trento
Context-Time(Q, December 14th)
Context-LOC(Q, Trento)
CORE JUSTIFICATION COMPLEMENTARY
CINEMA:Astra
Movie-hasCinema(“Titanic” Cinema:Astra)
Movie-director(“Titanic” “Cameron”)
Cinema-loc(Cinema:Astra, Trento)
Movie-time(“Titanic”, 8 pm)
Movie-price(“Titanic” 10 euros)
Scenario 1: Question/Answering on Structured Data

13. 13
Which cinema is showing Titanic by Cameron in Trento?
EAT CONSTRAINT CONTEXT
?CINEMA:X
Movie-hasCinema(“Titanic” Cinema:X)
Movie-director(“Titanic” “Cameron”)
Cinema-loc(Cinema:x Trento
Context-Time(Q, December 14th)
Context-LOC(Q, Trento
Titanic is showing today in Trento at Cinema Astra at 8 p.m.
Price of the ticket is 10 Euros.
CORE JUSTIFICATION COMPLEMENTARY
CINEMA:Astra
Movie-hasCinema(“Titanic” Cinema:Astra)
Movie-director(“Titanic” “Cameron”)
Cinema-loc(Cinema:Astra, Trento)
Movie-time(“Titanic”, 8 pm)
Movie-price(“Titanic” 10 euros)
Scenario 1: Question/Answering on Structured Data

14. Scenario 2: FAQ Retrieval
• Task: retrieve the most
similar FAQ to the user
question
• No need for deep text
interpretation
• Text-to-Text approaches
• The role of learning
technologies

15. TOPIC: Reasons for dissatisfaction in railway service
Int-448:Efficient service. Quick through security and check in.
But leg room in standard class was quite poor.
Int-202:Everything ran smoothly and well. Only complaint is
lack of leg room with seating with tables.
Int-275:Seating is very cramped – my journey has been very
uncomfortablewith the person next to me taking up most of the
space we have.
Extracting Fragments from Interactions

16. not happy with the catering coffee is awful
coffee in economy is
awful
no
refreshments
food on train is too
expensive
you charge too much for
sandwiches
food quality is disappointing
bad food in premier
not enough food selection provide veggie meals
not happy with the service
journey is too slow
no clear
information
not happy with the staff
staff is unfriendly no vegetarian food expand meal options
sandwiches are overpriced
sandwiches are too expensive
disgusting coffee is
served
they have horrible coffee
food is bad
Catering non
buono
Caffe’ pessimo
C’era un caffe’ terribile
Hanno servito caffe’ non
buono
Il caffe’ in classe
economica e’ cattivo
Nessun
rinfresco
pasti in prima
classe troppo cari
Panini troppo costosi
Panini costosissimi
Si paga troppo per I panini
Cibo di scarsa qualita’
La qualita’ del cibo e’
bassa
Cibo scadente in prima
classe
Scelta di cibon non
sufficiente
Espandere il menu
niente cibo
vegetariani
Aggiungere scelta vegetariana
servizionon
soddisfaciente
viaggio
troppo lento Informazioni
non chiare
Non
soddisfatto del
personale
Personale poco
amichevole
Organising Customer Interactions

17. Text-to-Text approaches
UIMA-CAS
Distance-based (EDITS)
Distance
Component
Edit Distance
ITALIAN
Tokenization, Lemma,
POS, dependency parsing
GERMAN
Token, POS, Lemma,
dependency parsing
ENGLISH
Token, Lemma, POS,
dependency parsing
WORDNET
Italian
German
English
Lexical component
Entailment rules
WIKIPEDIA
Italian
English
Classification-based (TIE)
Scoring
Component
Bag of Words
similarity
DISTRIBUTIONAL
SIMILARITY
English
German
Italian
Configurator
Transformation-based (BIUTEE + AdArte)
Alignment-based (P1EDA)
Algorithms
DERIVATIONAL
MORPHOLOGY
Italian
English
German
Alignment
Component
PHRASE
TABLES
Italian
English
German
Bulgarian
Token, Lemma, POS,
dependency parsing
• A platform for text-to-text
inferences
• Semantic Alignment
• Similarity
• Entailment
• Contradiction
• Based on machine learning
• FAQ retrieval at Evalita
2016:
http://qa4faq.github.io

18. Toward the Intelligent Chat
• From text-to-text (FAQ retrieval) to sequence-
to-sequence learning
• Learning from chats using neural networks
• First results: very realistic dialogue “style”
• Can we learn dialogue schema?
• How to integrate specific knowledge (e.g. in a
database)

19. Link…
• Associazione Italiana di Linguistica
Computazionale: ai-lc.it
• CLIC-it, Terza Conferenza Italiana di Linguistica
Computazionale
• Evalita: Valutazione di tecnologie del linguaggio
scritto e parlato per l’italiano: evalita.it
• NLP a FBK: http://hlt.fbk.eu