Poster for our ACL paper "Polyglot: Multilingual Semantic Role Labeling with Unified Labels". Abstract: We present POLYGLOT, a semantic role labeling system capable of semantically parsing sentences in 9 different languages from 4 different language groups. A core differentiator is that this system predicts English Proposition Bank labels for all supported languages. This means that for instance a Japanese sentence will be tagged with the same labels as an English sentence with similar semantics would be. This is made possible by training the system with target language data that was automatically labeled with English PropBank labels using an annotation projection approach. We give an overview of our system, the automatically produced training data, and discuss possible applications and limitations of this work. We present a demonstrator that accepts sentences in English, German, French, Spanish, Japanese, Chinese, Arabic, Russian and Hindi and outputs a visualization of its shallow semantics.

1. English Proposition Bank Frames
Generating Training Data for Multilingual SRL
Semantic Parsing of 9 Languages
English Chinese Spanish
buy.01
Roles:
A0: buyer (agent)
A1: thing bought (theme)
A2: seller (source)
A3: price paid (asset)
A4: benefactive (beneficiary)
German Japanese Russian
Training data generation pipeline
• Optional: Manual aliasing of TL verbs to English frames
• Filtered annotation projection (Akbik et al., 2015)
like.01
Roles:
A0: liker (experiencer)
A1: object of affection (theme)
give.01
Roles:
A0: giver (agent)
A1: thing given(theme)
A2: entity given to(recipient)
sell.01
Roles:
A0: Seller (agent)
A1: Thing Sold (theme)
A2: Buyer (recipient)
A3: Price Paid
A4: Benefactive
Challenges and open questions
• Source-language SRL errors
• Coverage: Do appropriate English
frames exist for all TL verbs?
• pouvoir (to be able to), sollen (to be
supposed to)
• Crowdsourced data curation
(Akbik et al., 2016)
• Design of crowdsourcing task
Alan Akbik and Yunyao Li
IBM Research - Almaden
POLYGLOT
Multilingual Semantic Role Labeling with Unified Labels
Idea: Use English Proposition
Bank Frames and Roles as
universal semantic labels
annehmen.01
(accept)
Roles:
A0: acceptor (agent)
A1: thing accepted (theme)
A2: accepted-from (source)
A3: attribute (attribute)
annehmen.02
(assume)
Roles:
A0: thinker (agent)
A1: thought(theme)
A2: attributive (source)
Predicate: annehmen
Example Target Language FrameAnnotation Projection
English, German, Chinese, French, Japanese, Spanish, Russian, Hindi and Arabic
semantic labels
(predicates + roles)
EN
unlabeled corpus
TL
Parallel corpus
semantic labels
(projected)
Annotation
projection
TL
Annotation projection Future work: Crowdsourced and expert data curation
Crowd
agrees?
Input
Crowdsourced data
curation
semantic labels
(crowd cannot curate)
semantic labels
(curated, final)
TL
TL
Expert data
curation
yes
no
Multilingual
aliases
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling. Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yunyao Li, Shivakumar Vaithyanathan and Huaiyu Zhu. ACL 2015.
Towards Semi-Automatic Generation of Proposition Banks for Low-Resource Languages. Alan Akbik and Yunyao Li. EMNLP 2016.
Evaluation