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<s>in Fig. 6.Fig. 5. Translation of the sentence“এই সমােজ বৃ লােকরা অচল পয়সা”Fig. 6. Translation of the sentence “এই সমােজ বৃ লােকরা অচল পয়সা”in Google translatorB. Accuracy RateWe observed that among 15000 sentences, a total of 12800sentences were correctly translated with our proposed model.The accuracy rate is the r... |
<s>M. R. Alam, and M. Islam, “A new verb based approachfor english to bangla machine translation,” in 2014 International Con-ference on Informatics, Electronics & Vision (ICIEV). IEEE, 2014, pp.1–6.[8] M. S. Arefin, L. Alam, S. Sharmin, and M. M. Hoque, “An empiricalframework for parsing bangla assertive, interrogative... |
<s>Paper Title (use style: paper title)Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based) Nayan Jyoti Kalita, Baharul IslamDepartment of CSE, Royal School of Engineering and Technology Department of IT, Gauhati University Guwahati, India {nayan.jk.123, islambaharul65}@gmail.com Abstract—Mach... |
<s>request is likewise comparable. A few dialects use regular script, particularly Devanagari. Hindi composed in the Devanagari script is the official language of the Government of India. English is likewise utilized for government notices and interchanges. India's normal writing proficiency level is 65.4 percent (Cens... |
<s>local to the locale of eastern South Asia known as Bengali, which embodies present day Bangladesh and the Indian state of West Bengali. With almost 230 million local speakers, Bengali is a stand out amongst the most prevalently talked languages on the planet. Bengali takes after Subject-Object-Verb word structures, ... |
<s>English. It is based on machine aided translation where template or hybrid HEBM is used. The HEBMT has the advantage of pattern and example-based approaches. It provides a generic model for translation between any two Indian languages pair [5]. 4. ANUBHARATI-II (2004): ANUBHARATI-II is a reconsidered form of the ANU... |
<s>sometimes group of words or whole sentence may have more than one meaning in a language. 4) Since all the natural languages are very vast so it is almost not possible to include all the words and transfer rules in a dictionary. 5) Since both Assamese and Bengali are free-word-order languages, so sometimes the transl... |
<s>order words in T and S does not affect P (T|S) and likelihood of (T|S) can be defined of conditional probability P (T, a/S) shown as P (S|T) = sum P(S, a/T). The sum is over the element of alignment set, A(S, T). Decoder This phase of SMT maximizes the probability of translated text. The words are chosen which have ... |
<s>find better weights we need to tune the translation system, which leads us to the next step. D. Tuning Tuning is the slowest part of the process. We have again collected a small amount of parallel data separate from the training data [8]. We are going to tokenize and truecase it first, just as we did the training pr... |
<s>of 16.3 from the parallel corpus (Bengali-Assamese) after translation. This is very small and may be because we have used a very small data set. BLEU score are not commensurate even between different corpora in the same translation direction. BLEU is really only comparable for different system or system variant on t... |
<s>BENGALI INFORMATION RETRIEVAL SYSTEM (BIRS)See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/337440845BENGALI INFORMATION RETRIEVAL SYSTEM (BIRS)Research · October 2019DOI: 10.5121/ijnlc.2019.8501CITATIONREADS3 authors, including:Some of the authors of this... |
<s>local machine, finding the desired information is a tedious process. Finding relevant information based on query, has some challenges such as word mismatch that is a sentence can be made in different ways, their meaning is same but structure is different and a question can be formulated in different ways utilizing s... |
<s>accomplished by the OCR scheme for Bangla[14] and the web handwritten OCR for Bangla [15]. Much research is performed previously on summarization, such as extracting Bangla sentences for document summarization [16]. In addition, knowledge extraction from Bangla Blogs and News [17], Bangla text extraction from real i... |
<s>verb processing, unknown word processing and removing punctuations and so on. 3.3. Pre-Processing We need to pre-process or normalize the informative documents and the questions through cleaning, verb processing, removing stop words, tokenization, lemmatization, and synonyms words processing . Basically, the unwante... |
<s>kind of Natural Language Processing technique and effective for use in various purposes such as text mining, answering questions or Chatbot etc.In Bengali natural language processing, there are few verbs that cannot be lemmatized by any system because of the limitation of lemmatization algorithms. For example, শেলে ... |
<s>Edit Distance can count the probability with the edit to its word (not lemma). If the probability P(edit|word) is greater than 50% (P(edit|word) > 50%) then it is counted as unknown words. International Journal on Natural Language Computing (IJNLC) Vol.8, No.5, October 2019 In order to process the unknown words, we ... |
<s>term (word÷length) of the sentence. Secondly, to figure out a relevant sentence by searching questions, IDF is pretty useful in this case. In TF all the words are treated as equal importance. But IDF determines the actual importance of a word in the document. Finally, we counted the desired TF-IDF by multiplication ... |
<s>সু্কে 0 0.046598 0 0 0 0 0 ঙ্গবলরাধ 0 0 0 0.026883 0 0 0 ১৯৬৬ 0 0 0 0.026883 0 0 0 িীবন 0 0.046598 0 0 0 0 0 ভারত 0 0 0.058248 0 0 0 0 শপে 0 0 0 0.026883 0 0 0.063543 হ়ে 0 0.046598 0 0 0 0 0 কায চ 0 0 0 0.026883 0 0 0 ১৯৭১ 0 0 0 0 0.053767 0 0 ২৬ 0 0 0 0 0.053767 0 0 রহমান 0.053767 0.006461 0 0.003727 0.007455 0.01... |
<s>of algorithms and methods such as lemmatization, anaphora resolution procedure, TF-IDF and Cosine Similarity. The whole actions have been processed with Bengali Language as part of BNLP. We have tested our proposed BIRS, noted the accuracy, compared the correct and incorrect results. In future, we have a plan to imp... |
<s>Vol.8, No.5, October 2019 [15] G. Fink, S. Vajda, U. Bhattacharya, S. K. Parui& B. B. Chaudhuri, (2010). “ Online Bangla word recognition using sub-stroke level features and hidden Markov models” International Conference. on Frontiers in Handwriting Recognition, ICFHR 2010, pp. 393-398. [16] K .Sarkar, (2012) “Benga... |
<s>Proceedings of the...D S Sharma, R Sangal and A K Singh. Proc. of the 13th Intl. Conference on Natural Language Processing, pages 99–108,Varanasi, India. December 2016. c©2016 NLP Association of India (NLPAI)Cross-lingual transfer parsing from Hindi to Bengali usingdelexicalization and chunkingAyan Das, Agnivo Saha,... |
<s>accu-rate parser (Saha and Sarkar, 2016). We wishto use this Hindi treebank to develop a Bengaliparser. Although our current work aims to developa parser in Bengali from Hindi, this may be takenup as a general method for other resource poor lan-guages. We also have access to a monolingualcorpus in Bengali and a smal... |
<s>use the induced interlingual word repre-sentation as augmenting features to train a delex-icalized dependency parser. Duong et al. (2015a)followed a similar approach where the vectors forboth the languages are learnt using a skipgram-likemethod in which the system was trained to predictthe POS tags of the context wo... |
<s>et al.(2009) used variations of the transition based de-pendency parsing. Mannem (2009) came up witha bi-directional incremental parsing and percep-tron learning approach and De et al. (2009) useda constraint-based method. Das et al. (2012) com-pares performance of a grammar driven parser anda modified MALT parser.3... |
<s>Bengali test sentences.It gives an UAS (Unlabelled Attachment Score) of65.1% (Table 2).We report only the UAS because the Bengaliarc labels uses AnnCorra tagset which is differ-ent from Universal Dependency tagset. The de-pendency lables in the UD and ICON treebanksare different, with ICON providing a more fine-grai... |
<s>2: Comparison of 1) delexicalized parsermodel and 2) parser using projected Bengali vec-tors.Delexi-calized(Baseline)ProjectedBengalivectors(Chen and Manning, 2014)parser 65.1 67.2Table 2 compares the UAS of word-level trans-fer for the 1) delexicalized parser model (Delex-icalized) and 2) the lexicalized Bengali pa... |
<s>and Hindi sentence that conveys the same meaning : "99people died due to earthquake in Patna".maraPatnay bhumikamper lok jayfale jon 99(a)marePatna bhukamp admike dwara 99mein(b)Figure 3: Word-level parse trees of the example Bengali and Hindi sentences (a) Bengali word-levelparse tree (b) Hindi word-level parse tre... |
<s>with Englishas the source language and achieved a UAS of58.9 on average while their baseline delexicalizedMSTParser parser using universal POS tag fea-tures gave an UAS of 55.14 on average. Duong etal. (2015b) also applied their method on nine tar-get languages and English as the source language.They achieved UAS of... |
<s>improvingthe quality of cross-lingual parsers. We observethat chunking significantly improves cross-lingualparsing from Hindi to Bengali due to their syntac-tic similarity at the phrase level. The experimen-tal results clearly shows that chunk-level transferof parser model from Hindi to Bengali is betterthan direct ... |
<s>Sarkar, and A. Basu. 2004. A hybridmodel for part-of-speech tagging and its applicationto bengali.Arjun Das, Arabinda Shee, and Utpal Garain. 2012.Evaluation of two bengali dependency parsers. InProceedings of the Workshop on MTPIL, pages 133–142, Mumbai, India, December. The COLING 2012Organizing Committee.Sankar D... |
<s>Long Papers - Volume 1, ACL ’12,pages 629–637, Stroudsburg, PA, USA. Associationfor Computational Linguistics.Joakim Nivre, Marie-Catherine de Marneffe, Filip Gin-ter, Yoav Goldberg, Jan Hajic, Christopher D. Man-ning, Ryan McDonald, Slav Petrov, Sampo Pyysalo,Natalia Silveira, Reut Tsarfaty, and Daniel Zeman.2016. ... |
<s>untitledSee discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/323059451Evaluation of machine translation approaches to translate English to BengaliConference Paper · December 2017DOI: 10.1109/ICCITECHN.2017.8281851CITATIONSREADS1124 authors, including:Some of t... |
<s>also another dilemma that most of them select a part of the source language for translating to the target language. In this paper English has been used as the source language and Bengali has been used as the target language because there are different types of sentences in both of these languages but the main focus ... |
<s>the two languages [10]. Fig. 1. Three stages of Transfer Approach C. Direct Approach The most primitive strategy is called the direct MT strategy, which is always between pairs of languages and based on good glossaries and morphological analysis. The direct approach lacks any kinds of intermediate stages in translat... |
<s>kind of sentences that are likely to be in the language . This is known as the language model ( ). The way sentences in s get converted to the sentences is called translation model ( / ). III. IMPLEMENTED METHODS Three (3) implemented methods of machine translation approach-i) Direct approach, ii) Corpus approach an... |
<s>best result with all 12 tenses in compare to Google translator. TABLE III. TRANSLATION OF 12 TENSES FROM ENGLISH TO BENGALI AND COMPARE WITH GOOGLE TRANSLATOR Name of Tense English Sentence Accurate Bengali Sentence Direct Approach Transfer Approach Corpus Based Approach Google Translator Present I play football আিম... |
<s>done by word by word and sentence by sentence. Whether it finds any word mismatch then counts word mismatch and if it finds any sentence mismatch then counts sentence mismatch. Finally the program counts word accuracy and sentence accuracy rate by the following equations 2 and 3, = – ∗ % (2) = – ∗% (3) Total 1027 se... |
<s>meaning of verb at first. Then most suitable meaning will be selected for the final translation. For example, the intermediate meaning of “I play football” in Corpus based method will be “আিম ফুটবল খিল খল”. Then after final matching the final translation will be “আিম ফুটবল খিল”. That’s why Corpus based method gives ... |
<s>1992. [11] Andy Way, and Nano Gough, “Comparing Example-Based and Statistical Machine Translation” in Natural Language Engineering, Vol. 11(3),pp.295-309,2005 View publication statsView publication statshttps://www.researchgate.net/publication/323059451 /ASCII85EncodePages false /AllowTransparency false /AutoPositio... |
<s>/CMTI7 /Cmti8 /CMTI8 /Cmti9 /CMTI9 /Cmtt10 /CMTT10 /Cmtt12 /CMTT12 /Cmtt8 /CMTT8 /Cmtt9 /CMTT9 /Cmu10 /CMU10 /Cmvtt10 /CMVTT10 /ColonnaMT /Colossalis-Bold /ComicSansMS /ComicSansMS-Bold /Consolas /Consolas-Bold /Consolas-BoldItalic /Consolas-Italic /Constantia /Constantia-Bold /Constantia-BoldItalic /Constantia-Ital... |
<s>/MSReferenceSansSerif-Bold /MSReferenceSansSerif-BoldItalic /MSReferenceSansSerif-Italic /MSReferenceSerif /MSReferenceSerif-Bold /MSReferenceSerif-BoldItalic /MSReferenceSerif-Italic /MSReferenceSpecialty /MSSong /MS-UIGothic /MT-Extra /MTExtraTiger /MT-Symbol /MT-Symbol-Italic /MVBoli /Myriad-Bold /Myriad-BoldItal... |
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<s>One-Expression Classification in Bengali and its role in Bengali-English Machine TranslationOne-Expression Classification in Bengali and its role in Bengali-English Machine Translation Apurbalal Senapati CVPR Unit, Indian Statistical Institute Kolkata, India apurbalal.senapati@gmail.com Utpal Garain CVPR Unit, India... |
<s>the recent times1 and therefore, one-expressions still have not got a chance to be looked upon. However, statistics show that in Indic languages like Bengali, one-expressions are used often. A study on 177-million-word FIRE Bengali Corpus2 shows that about 1.34-million words refer to one-expressions. Obviously, they... |
<s>-tar inflection), …]. The use of one-expression is quite frequently in Bengali. We investigated the frequency of one-expressions in the 177-million-word FIRE Bengali Corpus [6] and found that about 0.76% words (about 1.34 million words) in the corpus are one-expressions. This counts all morphological variations of o... |
<s>2006 one-expression and manually annotated these with one of the seven classes described above. The distribution of each class in the annotated corpus is shown in the TABLE I. We call this annotated dataset ℑr as this has been used to train a CRF-based classifier as explained in the next section. It is noted that th... |
<s>NN o ......................................................................... TABLE III. DESCRIPTION OF TRAINING DATA Column Type Description 3 http://mallet.cs.umass.edu/sequences.php 1 Document Id Contains the filename 2 Word number Word index in the sentence 3 Word Word itself 4 POS POS of the word 5 Classificat... |
<s>0 1 EQU 2 0 0 0 × 0 0 GEN 6 4 0 0 0 × 0 OTH 5 9 0 0 0 0 × X. EFFECT ON MACHINE TRANSLATION In the beginning of our discussion we show that as the one-expressions behave differently in different context, production of their right translation (for example, in English the Bengali one-expressions can be translated to a ... |
<s>of the major reasons behind its inaccuracies is the small size of the training data. We also plan to extend the present research for resolution of one-anaphors. For this work, we plan to classify the one-expressions as one-anaphor or not (two-class problem) and then resolve the one-anaphors by finding their correct ... |
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<s>M.Sc. Engg. ThesisTowards Achieving A Delicate Blending betweenRule-based Translator and Neural Machine Translator forBengali to English TranslationMd. Adnanul Islam (0416052015F)Submitted toDepartment of Computer Science and Engineering(In partial fulfilment of the requirements for the degree ofMaster of Science in... |
<s>suggestions regarding the writing and presentation of this thesis.Last but not the least, I remain ever grateful to my beloved parents, who always exist as sourcesof inspiration behind every success of mine.AbstractAlthough, a number of research studies have been done on natural language processing (NLP) indifferent... |
<s>Model . . . . . . . . . . . . . . . . . . 92.2.1 Background of GNMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.2.1 Embedding Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.2... |
<s>and Memory Optimization . . . . . . . . . . . . . . . . . . . . . . . 233.2.1 Approach 1: Plain Vocabulary including All Forms of Verbs . . . . . . . . . . . 233.2.2 Approach 2: Optimized Database with Semantic Analysis . . . . . . . . . . . . 243.2.3 Approach 3: Modified Levenshtein Distance . . . . . . . . . . . .... |
<s>. . . . . . . . . . . . . . . . . . . . 414.2.4 Custom Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2.5 Full Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.2.6 GlobalVoices Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... |
<s>. . . . . . . . . . . . . . . . . . . . . . 664.4.6 Results from Our Different Blending Approaches . . . . . . . . . . . . . . . . . 674.4.6.1 Results using Literature-based Dataset . . . . . . . . . . . . . . . . . 674.4.7 Results using Full Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.5 ... |
<s>98List of Figures1.1 Applications of translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1 Faulty translations of Google Translator . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Architecture of Unsupervised Neural Machine Translation [11] . . . . . . . . . . . . . . 72.3 Arc... |
<s>of a compound sentence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.12 Several forms of two different verbs in Bengali . . . . . . . . . . . . . . . . . . . . . . . 233.13 Database optimization using semantic analysis on different forms of a verb . . . . . . . 253.14 Mapping between concatenated strin... |
<s>. . . 394.2 Individual sentences for evaluating translations by our rule-based translator . . . . . . 404.3 Partial Bengali literature-based dataset (extracted from Al-Quran) . . . . . . . . . . . 424.4 Partial English literature-based dataset (extracted from Al-Quran) . . . . . . . . . . . 434.5 Partial Bengali voc... |
<s>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.21 Snapshots of translations generated by Google Translator for our example sentences inTable 4.5 (collected on or before August 30, 2019) . . . . . . . . . . . . . . . . . . . . 664.22 NMT versus only rule-based METEOR score . . . . . . . . . . . .... |
<s>. . . . . . . . . . . . . . . . . 784.36 Variation of TER scores with an increase in the number of implemented rules for fulldataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.37 Comparison of normalized performance scores with an increase in the number of im-plemen... |
<s>. . . . . . 183.2 Token translation using vocabulary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.3 Final tagging with translation containing necessary information about each token . . . 193.4 Commonly-used Bengali suffixes representing tenses . . . . . . . . . . . . . . . . . . . 203.5 Modifying ve... |
<s>Overall percentage (%) improvement over different parameters with respect to NMTfor literature-based dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83xiv4.14 Overall percentage (%) improvement over different parameters with respect to NMTfor full dataset . . . . . . . . . . . . . . . . . .... |
<s>by semantic basedparts of speech tagging, verb identification and stemming, and name identification by lemmatization.CHAPTER 1. INTRODUCTION 2We perform our study through exploring rule-based translation and neural machine translation - bothin isolation and in combination, through applying different blending approac... |
<s>3.In Chapter 4, we discuss the performance evaluation of our proposed mechanisms. Here, first, weshow our experimental settings and different datasets. Next, we discuss our performance evaluationmetrics (BLEU [19], METEOR [20], and TER [21]). Then, we present various experimental results(simulation outputs, graphs, ... |
<s>machine translation approach in recent years, showingsuperior performance on public benchmarks [1][11]. It is an end-to-end learning approach for auto-mated translation, with the potential to overcome many of the weaknesses of conventional translationsystems. In spite of the recent success of NMT in standard benchma... |
<s>their proposed chunk-based EBMT. According toFigure 2.3: Architecture of chunk-based EBMT [6]the architecture, first, given an input sentence, the system finds chunk (a sequence of words) sequencematches, and a chunk aligner finds their translations. Next, when no chunk match or no chunk align-ment is found, it find... |
<s>Translation (NMT) attempts to closely mimic that.Specifically, an NMT system first reads the source sentence using an encoder to build a “thought”vector (a sequence of numbers that represents meaning of the sentence). Then, a decoder processesthe sentence vector to perform a translation, as illustrated in Figure 2.4... |
<s>starting states.2.2.2.3 DecoderThe decoder must transform the learned internal representation of the input sequence into the correctoutput sequence. NMT can also use one or more LSTM layers to implement the decoder model. Thismodel reads the fixed sized output generated by the encoder model. The decoder also needs t... |
<s>research in the field of language processing, Bengaliremains little explored in the literature. Therefore, in this study, we specifically address some majorlimitations in Bengali to English translation along with Bengali language processing to some extent.These major limitations are discussed as follows.• None of th... |
<s>a Bengali sentence. If we geta paragraph as input, we recognize sentences by splitting the input paragraph through the sentenceCHAPTER 3. PROPOSED METHODOLOGY 16Figure 3.1: Mechanism of our proposed rule-based translatorterminating delimiters. Our considered sentence terminating delimiters are - ‘|’ and ‘;’. The inp... |
<s>accomplishes this task by its token tagging process, which we discuss next.3.1.3 Step-3: Token TaggingOur system performs the task of token tagging using the information from grammatical set of rulesfor Bengali sentences. In our token tagging, we identify position, PoS, number, and person for each ofthe tokens. Tabl... |
<s>formwill be + ‘ing’ formFuture Perfect 33 12/ 31/ 21/ 2shall have + ‘past participle’ formwill have + ‘past participle’ formTable 3.5: Modifying verbs based on tenses, persons, and numbers3.1.6 Step-6: Rearrange Words by Applying Grammatical RulesOur system has generated all the words of the translated sentence by n... |
<s>the tense and subject of the sentence applyingsemantic analysis as discussed earlier. Let us consider an example of a translated verb ‘eat’. We willprocess the verb as ‘is eating’, ‘ate’, ‘has eaten’, etc., based on the semantic analysis (using tokentagging table) of the sentence. This approach guarantees 100% accur... |
<s>a Bengali verb may have prefix and suffix assimilatedinto it based on tense, number, person, etc. Accordingly, instead of directly trying to match a non-standard form of verb with its standard form, first, the non-standard form is broken down into its rootword (stemming) in this approach. Afterwards, that root word ... |
<s>Rather theyCHAPTER 3. PROPOSED METHODOLOGY 28Figure 3.17: Translating names from Bengali to English using our proposed phonetic mappingconversion systemmean to emphasize the names or pronouns presenting a notion of supporting adjective or adverb.Thus, the tags have separate meanings and use in the sentences. If we d... |
<s>Different blending techniques between rule-based translation and NMT as explored inthis studychapter. GNMT works considerably well for translating between any pair of popular languages.However, NMT has its own major limitation in terms of generating accurate translations as shown inFigure 2.1 earlier. Thus, both rul... |
<s>More specifically, rule-based translator just further ameliorates theskeleton of the translated sentence that NMT has already built as shown in Algorithm 2.Algorithm 2 Blending Module for NMT followed by rule-based approach and rule-based followed byNMT approachprocedure PerformBlendingInput : sentence1← ArrayList o... |
<s>blending technique. This time, oursystem considers rule-based translation as ‘sentence1’ and NMT generated translation as ‘sentence2’in Algorithm 2. Major limitation of this technique is that NMT runs completely on its own. NMT cangenerate completely wrong words in different positions in a sentence during translatio... |
<s>scope ofthe rule-based translator. The more we add rules, the more types of sentences we can translate usingrule-based translator. Therefore, selection criteria can be made much more flexible and tricky in thissystem depending on performance analysis after incorporating more rules.CHAPTER 3. PROPOSED METHODOLOGY 36F... |
<s>claimed in [50].4.2 DatasetsDesigning and developing datasets has been one of the most challenging and time intensive tasks inour experimentation. For training the NMT reasonably, we require a large parallel corpus containingboth source language and target language. In our case, NMT requires such a corpus of Bengali... |
<s>rule-based translatorCHAPTER 4. PERFORMANCE EVALUATION 414.2.3 Literature-based DatasetUnlike rule-based translator, we require a large parallel corpus of Bengali-English sentence pairs inNMT. Therefore, we develop our literature-based dataset keeping NMT as the prime focus. It is achallenging task to collect and co... |
<s>to develop thecustom dataset, which is presented in Appendix. Size of our custom dataset is around 3,500 parallelsentences, which is so small that it cannot train an NMT system reasonably. Therefore, we do notCHAPTER 4. PERFORMANCE EVALUATION 46Figure 4.8: Partial Custom datasetuse this dataset independently in our ... |
<s>to check whether r × freq(r)becomes approximately a constant in all cases. The simplest way to show that Zipf’s law holds ina dataset is to plot the computed values and check whether the slope is proportionately downward.Here, instead of plotting freq(r) versus rank, it is better to plot log(r) in the X axis and log... |
<s>51• Reference 3: “It is the practical guide for the army always to heed the directions of the party.”It is clear that the good translation, Candidate 1, shares many words and phrases among these threereference translations, while Candidate 2 does not. Besides, note that Candidate 1 shares “It is a guideto action” wi... |
<s>candidate sentenceachieves a modified bigram precision of 0.We penalize candidate translations longer than their references using the modified n-gram precisionmeasure. Here, we introduce a multiplicative brevity penalty factor so that a high-scoring candidatetranslation must now match the reference translations in l... |
<s>possible unigram mappings - one mapping the occurrence of “computer” inthe system translation to the first occurrence of “computer” in the reference translation, and anothermapping it to the second occurrence. Here, different modules map unigrams based on different criteria.The “exact” module maps two unigrams if th... |
<s>and a reference translation in the followingexample.Example 3.• Candidate: on the mat sat the cat• Reference: the cat sat on the matIn the above example, the number of mapped unigrams in the candidate sentence is 6, and the totalnumber of unigrams in the candidate sentence is 6. Therefore, unigram precision, P is 1 ... |
<s>sentence. However, TER does not consider this an exact match. First, the phrase “thisweek” in the candidate is in a shifted position (at the beginning of the sentence rather than after theword “denied”) with respect to the reference. Second, the phrase “Saudi Arabia” in the referenceappears as “the Saudis” in the ca... |
<s>finding of the evaluation in the nextsubsections.4.4.1 Results from Our Proposed Rule-based TranslatorIn our rule-based translator, we consider all types of sentences covering basic simple, complex, andcompound sentences. Initially, we implement simple sentences in our system through adding somebasic rules for formi... |
<s>observation regarding emphasized subjectidentification is that the emphasizing tag itself may be the part of a valid name (subject) as mentionedearlier in the previous chapter (Chapter 3). In such (less frequent) cases, our system discards thattag (which is not actually any tag) from the valid name leading to a faul... |
<s>comparison, we present that our rule-based translator performs better than Google Translator incase of sentences whose rules have already been implemented in our system so far. We show examplesof such improvements achieved by our rule-based translator over Google Translator in Table 4.6.Table 4.6: Comparison between... |
<s>4). Ideally, BLEU score is considered for n-gram model where n=4. In all cases including n=4, the blending of ‘NMT followed by rule-based’outperforms all other alternatives.n-gram NMT Rule-based NMT+rule-based Rule-based+NMTNMT orrule-based1-gram 31.46 31.74 46.07 25.59 31.352-gram 17.18 10.70 25.19 7.56 17.373-gram... |
<s>NMT generated translationsmostly. However, this approach performs at least as good as classical NMT.We can clearly notice that light red lines exceed deep blue lines for most of the sentences inCHAPTER 4. PERFORMANCE EVALUATION 72Figure 4.28: NMT versus NMT or rule-based METEOR scoreFigure 4.29: NMT versus NMT or ru... |
<s>improved rule-based translation after generating translation by NMT.This is why, we notice such similarity between these two curves.Similarly, we illustrate variation of METEOR scores with respect to the number of added rules.Figure 4.31 presents the results for only rule-based approach, NMT, and ‘NMT followed by ru... |
<s>Variation of BLEU scores with an increase in the number of implemented rules for fulldatasetBesides, we also present another combined graph (Figure 4.37) containing normalized value ofall the metrics for both rule-based approach and ‘NMT followed by rule-based’ approach using ourfull dataset. Figure 4.37 reflects th... |
<s>and ‘NMT followed by rule-based’ forliterature-based datasetNext, we also explore time overheads of different approaches with respect to an increase in thenumber of implemented rules using our full dataset. Figure 4.39 shows three different curves (rule-based, NMT, and NMT followed by rule-based) generated using our... |
<s>different approaches withrespect to NMT.Parameters NMT+rule-based Rule-based+NMT NMT or rule-basedBLEU 32% -86% 6%METEOR 57% -9% 5%TER 10% -3% 1%Table 4.14: Overall percentage (%) improvement over different parameters with respect to NMT forfull datasetSimilarly, Table 4.15 and Table 4.16 reflect the results (averag... |
<s>our best approach (BLEU = 16.43) lagsbehind their proposed approach (BLEU = 17.43) in terms of overall performance score because of ourinsufficient dataset again. They trained their system with a large dataset containing 197,338 parallelBengali-English sentences, which is more than 16 times larger than our current d... |
<s>of training stepsChapter 5Analogy to Human Behaviour: ACasual Cross Checking to OurProposed Methods and Their ResultsAt this point, we perform a casual cross checking to our proposed methods and their results withrespect to human behaviour. Note that the idea of our proposed translation approaches actually comesfrom... |
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