The poster describes the state of the art and the open challenges in MWE processing in Machine
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Monti, Johanna

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MONTI, JOHANNA

ARCHIVIO ISTITUZIONALE DELLA RICERCA-UNIVERSITA' DEGLI STUDI DI NAPOLI &quot;L'ORIENTALE&quot;

MWE processing in Machine Translation: State of the Art and Open Challenges

Università degli Studi di Napoli L'Orientale: CINECA IRIS

MWE processing in Machine Translation: State of the Art and Open Challenges Johanna Monti Department of Human and Social Sciences  University of Sassari Italy   PARSEME meeting – Warsaw 16-19 September 2013  MWE: a hard nut to crack for MT?  2 to go to the dogs= andare in malora to get married = sposarsi To look through = guardare attraverso Johanna Monti     MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013   MWE: a hard nut to crack for MT?  3 Combat photographer = reporter di guerra  To take pictures  =  fotografare    Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                            PARSEME meeting – Warsaw 16-19 September 2013      Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                               PARSEME meeting – Warsaw 16-19 September 2013    MWE: a hard nut to crack for MT? MWE in MT: still a problem! Very frequent phenomenon both in standard and domain specific languages Unpredictable translations mistranslation in MT due to fragmentation and literal translations Semantic idiosyncracy:  different degrees of compositionality Morpho-syntactic idiosyncracy: formal variations with dependencies of elements even if non-contiguous 5 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013     Back to the past?   «The only way for a machine to treat idioms  successfully is not to have idioms» (Bar-Hillel 1952) 6 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                              PARSEME meeting – Warsaw 16-19 September 2013   MWE: different types   Idiomatic expressions • En. Hold your tongue ( It. Frena la tua lingua)  Proverbs • En. The early bird gets the worm ( It. Chi dorme non piglia pesci)  Word compounds  • adjectives = En. good-looking  It. di bell’aspetto • prepositions = En. in order to  It. per  • adverbs  = En. arm in arm  It. sottobraccio • conjunctions  = En. in spite of  It. nonostante.  • Nouns = En. credit card  It. carta di credito • Verbs =  En. Kick the bucket  It. tirare le cuoia Noun compounds • Nouns modified by:  • nouns = En. credit card  It. carta di credito,  • Adjectives =  En. perfect pitch  It. orecchio assoluto  • adjectival locutions = En. amount of time  It. quantità di tempo.  Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    7 MWE: different types Verb compounds • Verbs modified by: • particles = En. give up  It. rinunciare,  • prepositions = En. adapt to  It. adattarsi a,  • nouns = En. advance a project  It. presentare un progetto • Phrasal verbs • En. give away  It. dar via, donare;  • En. give back  It. restituire, rendere, ridare;  • En. give in  It. consegnare, arrendersi;  • En. give off  It. emettere, sprigionare;  • En. give out  It. distribuire;  • En. give over  It. dedicare, consegnare;  • En. give up  It. cedere, arrendersi, smettere;  • En. give way  It. cedere • Light verbs or support verb constructions • En. = to give a presentation  (to present)  It.  Fare una presentazione (presentare) Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    8 MWE: different types Term compounds • Various types of domain-specific compounds, but mainly noun compounds.  • close relationship between terminology and multi-words and, in particular, word compounds (90% of  domain-specific terms) • mono-referential :  pay scale in the financial domain, = “the different levels of pay for a particular job, relating to different degrees of skill or experience”  It. scala dei salari.  • Their meaning, cannot be directly inferred by a non-expert from the different elements of the compounds. • Complex and varied  morpho-syntactic and semantic behaviour with unpredictable translations. Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    9 MWE: different types Collocations •Any statistically significant co-occurrence” of words (Sag et al.  2002)  : non-casual, restricted, arbitrary and recognisable combinations of words (collocates)  •usually semantically compositional, •particularly relevant in MT since they cannot always be translated literally: En. anticipate the salary  It. anticipare lo stipendio; En. anticipate a pleasure  It. pregustare un piacere; En. anticipate Ving  IT. prevedere di Vinf.  •Unpredictability of word co-occurrence on the basis of syntactic or semantic rules: En. I did my homework  vs *I made my homework.  •The translation of collocations requires a correct interpretation of their meaning which is determined by the co-text.  En. anticipate a pleasure  It * anticipare un piacere  (Google Translate). Other types of MWE •Named Entities: En.  Economic Council •Lexical bundles: En: I believe that, as much if not more than, if I were you Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    10 MWE as part of a continuum Elia & D'Agostino (1998) identify 4 types of combinations of phrases or sentences: high degree of variability of co-occurrence (free internal distribution)  dirty water, clean water limited degree of variability of co-occurrence (restricted internal distribution) natural water 3. no or almost no variability of co-occurrence (fixed internal distribution)  heavy water 4. no variability of co-occurrence (proverbs) all good things come to he who waits 11 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    Some properties 12 Non-substitutability: one element of the MWE cannot be replaced without a change of meaning or without obtaining a non-sense (in deep water → in hot water; gas chamber → *gas room); Non-expandability: insertion of additional elements is not possible (get a head start → *get a quick head start);  Non-reducibility: the elements in the MWE cannot be reduced and pronominalisation of one of the constituents is also not possible (take advantage → *what did you take? advantage; *Did you take it?;  Non-translatability: the meaning cannot be translated literally as is the case for many idioms and proverbs ( En. It’s raining cats and dogs → It. *Sta piovendo cani e gatti), as well as other types of MWUs (It. compilare un modulo → En. *Compile a module); Invariability: Invariability can affect both the morphological and the syntactic level. Inflectional variations of the constituents of the MWUs are not always possible. Invariability affects both the head elements and its modifiers (fish out of water → *fishes out of water; dead on arrival → *dead on arrivals; in high places → *in high place); syntactical variations inside an MWE may also not be acceptable (credit card  *card of credit); Non-displaceability: displacement and a different order of constituents are not possible (wild card → *is wild this card?) - (back and forth → *forth and back);  Institutionalisation of use: certain word units, even those that are semantically and distributionally "free", are used in a conventional manner. The Italian expression in tempo reale (a loan translation of the English expression in real time) is an example of this feature since its antonym *in tempo irreale (*in unreal time) seems to be unmotivated and not used at all. Non-substitutability:  in deep water → in hot water; gas chamber → *gas room Non-expandability: get a head start → *get a quick head start Non- reducibility: take advantage → *what did you take? advantage; *Did you take it?;  Invariability: fish out of water → *fish s out of w ter; dead on rriv l → *dead on arrivals; in high places → *in high place; credit card  *card of credit; Non- displaceability: wild card → *is wild this card?-  back and forth → *forth and back Institutionalisation of use:  in tempo reale (a loan translation of the English expression in real time)  vs *in tempo irreale (*in unreal time)  Non-translatability: E n.  It’s raining cats and dogs → It. *Sta piovendo cani e gatti,  It. compilare un modulo → En. *Compile a module Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    MWE in Machine Translation The main translation problems in MT  are connected to multi-word units. (Hurskainen, 2008) The handling of multi-word units in MT is a well-known problem.        Recognition of MWE necessary to preserve meaning and produce accurate translations (Villavicenzio et al., 2005)      Multi-word units significantly contribute to the robustness of the machine translation systems (Váradi, 2006) 13 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    Different approaches to MWE • Lexical approach (electronic dictionaries)  suitable for contiguous MWE • Compositional approach (rules)  useful for translating MWE not coded in the system dictionary and for translating verbal constructions Rule-Based Machine Translation (RBMT) • Analogy principle – reuse of translation stored in the system • Alignment - uses raw (un-annotated) input data to extract correspondences from large parallel corpora • Sub-sentential alignment from parallel bilingual corpora  Example-based Machine Translation (EBMT) 14 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    Different approaches to MWE • MWE as a problem of automatically learning and integrating translations of very specific MWE categories, word alignment, or word sense disambiguation (WSD) • Able to identify MWE with no or almost no variability in co-occurrence among words • Shortcomings in identifying MWE with a high and limited degree of variability of co-occurrence Statistical Machine Translation (SMT) • Integration of the statistical model with linguistic knowledge bases (Chiang, 2005; Marcu et al., 2006; Zollmann and Venugopal, 2006, …),  Hybrid Machine Translation (HMT) 15 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    An RBMT approach to MWE processing: OpenLogos  Linguistic  Knowledge Base   Dictionaries (source, target and transfer) Semantico-syntactic rules - analysis, transfer and generation Semantic Tables SEMTAB - language-pair specific rules OpenLogos system  (Scott, 2003; Scott and Barreiro, 2009; and Barreiro et al., forthcoming)  16 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    SAL - Semantico-syntactic Abstraction Language • Taxonomy: 3 levels organized hierarchically: Supersets / Sets / Subsets Semantico-Syntactic continuum from NL word to Word Class • Literal word:   airport • Head morph:   port • SAL Subset:  Agfunc (agentive functional location) • SAL Set:  func  (functional location) • SAL Superset:  PL (place) • Word Class:  N SAL combines both the lexical and the compositional approaches in order to process different types of MWE An RBMT approach to MWE processing: OpenLogos  An RBMT approach to MWE processing: OpenLogos  Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    SEMTAB rules • key component in MWE processing in OPENLOGOS • able to handle different types of MWE  • invoked after dictionary look-up and during the execution of target transfer rules to solve analysis and lexical ambiguity problems • identification, disambiguation and translation of MWE in context • single deep-structure rules match multiple surface-structures and produce correct target transfers Some examples:  • En. mix up (VT) N (human) in  It. confondere N in  • En. mix up (VT) N (ingredient)  It. mescolare N                                       • En. mix up (VT) N (medicine)  It. preparare N                                         • En. mix up (VT) with  It. confondere con                                             • En. mix up (VT) N (human,info) with  It. confondere N con                              Johanna Monti 19 Format RES1 RES2 P1 P2 P3 P4 S T4 T3 T1 T2 GEN Format SEMTAB Target Rules SEMTAB SEMTAB SAL Rules Target Rules Target Rules Input Output SEMTAB OpenLogos Architecture Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    MWE in SMT: state of the art Integration of PB-MT models with linguistic knowledge MRD and glossaries as phrases in the phrase-based table or to substitute unknown words  Integration of  syntactic and semantic structures Phrase-based  translation model (Zens et al., 2002; Koehn et al., 2003) and the maximum entropy approach (Och and Ney, 2002). flat-structured model MWE marked and aligned as parts of consecutive phrases /not treated as special cases Able to handle many-to-many mappings Problems with discontinous MWE Word-for-word approach and the noisy channel approach (Brown et al., 1993) flat-structured models elements of syntax are captured implicitly Inability to handle many-to-many correspondences 20 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    Phrase tables in SMT " , per la gestione del presente ||| " for the management of this ||| 0.245841 0.000386953 0.245841 0.0788203 2.718 ||| ||| 1 1 " , per la gestione del ||| " for the management of ||| 0.245841 0.000632227 0.245841 0.0841843 2.718 ||| ||| 1 1 " , per la gestione ||| " for the management ||| 0.245841 0.00310736 0.245841 0.143357 2.718 ||| ||| 1 1 " , per la quale sono richiesti ||| " , requiring ||| 0.718868 3.33037e-08 0.718868 0.00289219 2.718 ||| ||| 4 4 " , per la ||| " for the ||| 0.0491683 0.00479878 0.245841 0.210926 2.718 ||| ||| 5 1 " , per ||| " for ||| 0.0491683 0.039521 0.245841 0.339868 2.718 ||| ||| 5 1  21 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    MT processing some recent approaches 22 • MWE should be identified and grouped with the corresponding translations prior to the alignment process Lambert & Banchs [2006] • Construction of the phrase tables by means of bilingual dictionaries to improve SMT. Wu et al. [2008] • The integration of domain-specific bilingual MWE significantly improves translation.  Zhixiang Ren et al. [2009] • The knowledge about MWUs produces significant improvements Korkontzelos & Manandhar [2010] • The integration of contiguous MWUs and their translation improves translation quality. . Bouamor et al. [2011] Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    Some good news from the MT community  Growing attention to MWE processing in MT and Translation Technologies  Acknowledgement  that it is not possible to create large-scale applications without properly handling MWEs of all kinds. MTSummit 2013 workshop on Multi-word Units in Machine Translation and Translation Technology Closer interaction between NLP researchers, experts in phraseology (including computational phraseology) , terminologists and translation practitioners Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    23 Future challenges  The MWE problem should be approached taking into account the differences between the various MWE types Need of specific corpora for MT evaluation Comparison and linguistic evaluation of different MT approaches to specific MWE typologies (Barreiro et al. 2013) in order to assess positive and negative aspects Hybrid approaches to MWE processing in MT Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013    24  Thank you for your kind attention!  25 Johanna Monti  MWE processing in Machine Translation: State of the Art and Open Challenges                                             PARSEME meeting – Warsaw 16-19 September 2013       Questions? 26 

https://unora.unior.it/retrieve/handle/11574/170143/55468/WG3-Monti%20%281%29.pdf

MWE processing in Machine Translation: State of the Art and Open Challenges

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ARCHIVIO ISTITUZIONALE DELLA RICERCA-UNIVERSITA' DEGLI STUDI DI NAPOLI "L'ORIENTALE"

Università degli Studi di Napoli L'Orientale: CINECA IRIS

MWE processing in Machine Translation: State of the Art and Open Challenges

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ARCHIVIO ISTITUZIONALE DELLA RICERCA-UNIVERSITA' DEGLI STUDI DI NAPOLI &quot;L'ORIENTALE&quot;

Università degli Studi di Napoli L'Orientale: CINECA IRIS

ARCHIVIO ISTITUZIONALE DELLA RICERCA-UNIVERSITA' DEGLI STUDI DI NAPOLI "L'ORIENTALE"