A Transfer-rule Based Verb Phrase Translation from English to Tamil

Abstract

Building a machine translation (MT) between non-cognate languages always poses number issues as there are lots of translation divergences involved. In transfer-based MT, a systematic way of formulating transfer rules are required to handle linguistic di erences between languages. This paper explains three-stages in which the transfer-based machine translation (MT) are built for translating verb phrases from English to Tamil.

Keywords

• machine translation rules
• Linguistic Typology of Verb Phrases
• English and Tamil

Introduction

Machine translation is one of the challenging tasks in NLP as it involves a deep understanding of the source text and generating the acceptable target language automatically. Further, translation between non-cognate languages requires more e orts as there are more divergences i.e. cross-linguistic di erences [ 1 ] which affects the well-formedness of the target-language generation. In this paper, an effort towards building transfer rules for automatic verb phrase translation (VPT) from English to Tamil is attempted. This paper explains three-stages in which the transfer-based machine translation (MT) are built for translating verb phrases from English to Tamil. { The nite structure of the verb phrase in English and Tamil is: English: Model+Auxiliary/-ies+ Main Verb Tamil: Main Verb + Auxiliary/-ies+ Model+ Person-Number-Gender (PNG) Agreement { Negation is expressed through in ection on verbs in Tamil whereas through auxiliary in English. { In Tamil, the copula verb aku `to be' optionally occurs in the sentences of nominal predicates, whereas the copula is obligatory in English. { Compound verbs are constructed where sequences of a `polar' verb followed by one or more of `vector' verbs in Tamil. The polar verb is realized in Verbal participle or in nitive form in Tamil, unlike English. { In Causative construction, the periphrastic causative auxiliary verb -vai occurs in Tamil. Whereas the causative verb precedes the main verb in English. { Re exive and reciprocity are expressed through the auxiliary verb -ko.l and optionally through re exive pronouns in Tamil. Whereas, English uses pronouns to express the same. { Conjunct verbs (noun plus light verb) are more productive in Tamil when compared to English. { Non- nite verbs which head the subordinate clause in ects for verbal participle, in nitive, conditional and concessive forms in Tamil, unlike English. { In relative clause construction, Unlike English, Verbs in its adjectival participle form occurs before the noun phrase in Tamil. { Clitics such as interrogative, dubitative, emphatic and inclusive markers are added with verbs in the end position in Tamil, unlike English where these markers are expressed by di erent elements. 3

English-Tamil MT: A Review

Number of activities in building MT between English-Tamil are attempted by various groups and researchers in India. It includes Anuvadaksh (English to Indian Language Machine Translation System), Soman and Menon et.al [ 9 ], Poornima et.al [ 5 ], Saravanan [ 8 ], Pandian and Kathirvel [ 3 ], Ramaswamy et.al [ 7 ], Kumar et.al [ 2 ], Rajeswari et.al [ 6 ] to name a few. In this paper, an attempt is made in building transfer-based approach to MT between English-Tamil. 4

Algorithm for Verb Phrase Translation This section reports the algorithm used in verb phrase translation from English to Tamil. The algorithm used in building VPT consists of three stages: 4.1

Identi cation of Verb Phrase (VP) and its subject (nsubj) This stage identi es the verb phrase from the shared map le and also identi es the subject(s) (nsubj) of the sentence using the dependency-based parser. The nsubj is identi ed for their PNG features and the same is percolated to the verb to which it is identi ed as nsubj. The Tense (T) information is retrieved from POS of the head verb. The algorithm is given below: else else 1: Get Input Sentence 2: Identify Verb Phrase (VP) from shared Map le 3: Call Parser ( nltk.parse.stanford) 4: Find `nsubj' form Parse output match nsubj with NP list (consisting GNP features)

If found get PNG feature add default PNG feature (3,sg,n) 5: Find head verb of VP and its nsubj from Parse output

If found percolate nsubj PNG features to head verb add default PNG feature (3,sg,n) to head verb 6: Identify T based on POS output of Parse Algorithm 1: Identi cation of VP and nsubj 4.2

Stage 2: Transfer Rules

This stage involves transferring the structure and lexical items of English to Tamil. Using nltk lemmatizer, the verb root `VR' is identi ed. The structure of verb phrases is transferred to Tamil based on Rules (28 rules as an initial attempt) that are compiled in mapping English-Tamil. The lexical substitution from English to Tamil is executed in this stage. The algorithm is given below: 1: Get head verb 2: Identify verb root (VR) (lmtzr.lemmatize) 3: check head verb co-occurrence (e.g. Auxiliary verbs, Model verbs, Negation, Wh-question etc.,) if found

Apply Rules to reorder them 4: Identify Main and Subordinate clauses from Parse cues if found a subordinate clause

Apply Rules to provide appropriate TAM 5: match VR in English to Tamil verb dictionary if found

Substitute VR with equivalent Tamil else

Transliterate English VR into Tamil Algorithm 2: Transfer Rule Application

Stage 3: Generation

Generating well-formed wordforms based on identi ed PNG and TAM features in Tamil is attempted in this stage. The morphological generator for Tamil [ 4 ] is used for generating the Tamil verbs. The algorithm is given below: 1: Get Tamil VR, PNG and TAM features 2: Call Tamil Morphological Generator (TMG) 3: Input `VR,lcat (v), G, N, P,TAM' to TMG 4: Get the output Algorithm 3: Generation 5

Experiments and Results

The current MT system is evaluated by the coordinators of shared task on verb phrase translation in English and Indian Languages (VPTIL). The total number of training VPs in sentences received are 2275 and the total of testing VPs in sentences are 1869. The scoring criteria (see Table 1) and results (see Table 2) obtained are given below: The system performance can be improved by the improvement in transfer-rules, source language analysis modules, and target language generation modules. A robust lexical substitution is also required for the e ective mapping of source language verb root to the target language of the system. The precision of the system reveals that the transfer rule-based approach to MT between EnglishTamil performs well and can be improved further with the addition of new rules.

Abbreviations: VP- Verb Phrase; nsubj- Subject of verb phrase; PNG Person-Number-Gender markers; POS- parts-of-speech tagging; TAM- tense, aspect and model marker(s); lcat- lexical category; TMG- Tamil Morphological Generator; VR- verb root

Acknowledgment: The team acknowledges the coordinators of shared-task of VPTIL for their inputs.

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