=Paper=
{{Paper
|id=Vol-2266/T6-4
|storemode=property
|title=A Transfer-rule based Verb Phrase Translation from English to Tamil
|pdfUrl=https://ceur-ws.org/Vol-2266/T6-4.pdf
|volume=Vol-2266
|authors=Parameswari K,Nagaraju V,Angeline Linda K
|dblpUrl=https://dblp.org/rec/conf/fire/KVK18
}}
==A Transfer-rule based Verb Phrase Translation from English to Tamil==
https://ceur-ws.org/Vol-2266/T6-4.pdf
A Transfer-rule Based Verb Phrase Translation
from English to Tamil
Parameswari K.1 , Nagaraju V.2 , and Angeline Linda K.1
1
University of Hyderabad
2
eBhasha Setu Language Services
{parameshkrishnaa, vpp.nagaraju1234,angelineal1996}@gmail.com
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 formu-
lating transfer rules are required to handle linguistic differences 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 · divergence · verb phrase · transfer
rules
1 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
efforts as there are more divergences i.e. cross-linguistic differences [1] which af-
fects the well-formedness of the target-language generation. In this paper, an ef-
fort 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.
2 Linguistic Typology of Verb Phrases: English and
Tamil
The configuration of verb phrases in English and Tamil show lots of differences
which precludes the straightforward mapping of lexical and grammatical ele-
ments between these languages. Tamil is known for agglutinating morphology
and encodes various grammatical information as suffixes on verbs. Whereas En-
glish is known as morphologically poor language, hence the grammatical infor-
mation is realized as different words in strict word-order.
This major linguistic typological differences of verb phrases in English and
Tamil are listed below:
2 Parameswari K. et al.
– The finite 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 inflection on verbs in Tamil whereas through
auxiliary in English.
– In Tamil, the copula verb āku ‘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 infinitive form in Tamil, unlike English.
– In Causative construction, the periphrastic causative auxiliary verb -vai oc-
curs in Tamil. Whereas the causative verb precedes the main verb in English.
– Reflexive and reciprocity are expressed through the auxiliary verb -kol. and
optionally through reflexive pronouns in Tamil. Whereas, English uses pro-
nouns to express the same.
– Conjunct verbs (noun plus light verb) are more productive in Tamil when
compared to English.
– Non-finite verbs which head the subordinate clause inflects for verbal par-
ticiple, infinitive, conditional and concessive forms in Tamil, unlike English.
– In relative clause construction, Unlike English, Verbs in its adjectival par-
ticiple 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 different 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 In-
dian Language Machine Translation System), Soman and Menon et.al [9], Poorn-
ima 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 Identification of Verb Phrase (VP) and its subject (nsubj)
This stage identifies the verb phrase from the shared map file and also identifies
the subject(s) (nsubj) of the sentence using the dependency-based parser. The
nsubj is identified for their PNG features and the same is percolated to the verb
to which it is identified as nsubj. The Tense (T) information is retrieved from
POS of the head verb. The algorithm is given below:
English-Tamil Transfer Verb Phrase MT 3
1: Get Input Sentence
2: Identify Verb Phrase (VP) from shared Mapfile
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
else
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
else
add default PNG feature (3,sg,n) to head verb
6: Identify T based on POS output of Parse
Algorithm 1: Identification 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 identified. 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
4 Parameswari K. et al.
4.3 Stage 3: Generation
Generating well-formed wordforms based on identified 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:
Table 1. The Scoring Criteria
Completely Correct Score 4
TAM and PNG Correct Score 3
Correct root and TAM partially correct Score 2
Correct root and wrong TAM Score 1
Completely Incorrect Score 0
Table 2. Results of English-Tamil MT
Precision 20.77%
Recall 28.95%
6 Conclusion
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 effective mapping of source
English-Tamil Transfer Verb Phrase MT 5
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 English-
Tamil 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, as-
pect 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.
References
1. Dorr, Bonnie Jean. Machine Translation: a View from the Lexicon. Massachusetts:
MIT press. (1993)
2. Kumar A.M., V. Dhanalakshmi, K. P. Soman and S. Rajendran, Factored statistical
machine translation system for English to Tamil language, Pertanika J. Soc. Sci.
Hum. 22 1045–1061 (2014).
3. Pandian L.S. K. Kadhirvelu, Machine translation from English to Tamil using
hybrid technique, Int. J. Comput. Appl. 46 (2012).
4. Parameswari, K. An Implementation of Apertium Morphological Analyzer and Gen-
erator for Tamil. Language in India 11. 71–75 (2011).
5. Poornima C., V. Dhanalakshmi, M. Anand Kumar K. P. Soman, Rule based sen-
tence simplification for English to Tamil machine translation system, Int. J. Comput.
Appl. 25 (2011).
6. Rajeswari S., P. Sethuraman K. Krishnakumar, English to Tamil machine transla-
tion system using universal networking language, Sādhana 41 607–620 (2016).
7. Ramasamy L., O. Bojar and Z. abokrtsk, Morphological processing for English-
Tamil statistical machine translation, in: Proceedings of the Workshop on Machine
Translation and Parsing in Indian Languages, pp. 113–122, (2012).
8. Saravanan S., English to Tamil machine translation: rule based approach, LAP LAM-
BERT Academic Publishing, (2012).
9. Soman, K.P. A. G. Menon, English to Tamil machine translation system, in: 9th
Tamil Internet Conference (INFITT), Chemmozhi Maanaadu, Coimbatore, India,
(2010).