lakṣyārtha (Indicated Meaning) of Śabdavyāpāra (Function of a Word) framework from kāvyaśāstra (The Science of Literary Studies) in Samskṛtam : Its application to Literary Machine Translation and other NLP tasks (2021)

Abstract: A key challenge in Literary Machine Translation is that the meaning of a sentence can be different from the sum of meanings of all the words it possesses. This poses the problem of requiring large amounts of consistently labelled training data across a variety of usages and languages. In this paper, we propose that we can economically train machine translation models to identify and paraphrase such sentences by leveraging the language independent framework of Śabdavyāpāra (Function of a Word), from Literary Sciences in Saṃskṛtam, and its definition of lakṣyārtha (‘Indicated’ meaning). An Indicated meaning exists where there is incompatibility among the literal meanings of the words in a sentence (irrespective of language). The framework defines seven categories of Indicated meaning and their characteristics. As a pilot, we identified 300 such sentences from literary and regular usage, labelled them and trained a 2d Convolutional Neural Network to categorise a sentence based on the category of Indicated meaning and finetuned a T5 to paraphrase them. We compared these paraphrased sentences with those paraphrased by a T5 finetuned on Quora Paraphrase dataset of 400,000 sentence pairs. The T5 finetuned on the Indicated meaning examples performed consistently better. Moreover, a Google Translate translates these paraphrased sentences accurately and consistently across languages

## introduction consider a sentence from rabindranath tagore's gitanjali "drunk by the joy of singing, i forget myself", and its translation to various languages using google translate foot_0 . refer to for the example above, while in some languages the usage could be appropriate, the translation is not very clear in quite a few languages. on the other hand, if we paraphrase the original sentence to "overjoyed by singing, i forget myself", then ## śabdavyāpāra (function of a word) and lakṣyārtha (indicated meaning) as stated above, according to śabdavyāpāra framework meanings words convey are categorised as vācyārtha (expressed meaning), lakṣyārtha (indicated meaning) and vyaṅgyārtha (suggested meaning). while 'expressed meaning' is the straightforward sum of meanings of all the words in the sentence, in 'indicated meaning' or 'suggested meaning' the meaning of the sentence is not the sum total of the meanings of all the words and differ based on the various nuances of language, local culture etc., expressive: that which denotes the direct conventional (or dictionary) meaning is the expressive word. in ordinary parlance, a word denotes something by convention of the given language. where the conventional denotation is not known, there is no comprehension of the meaning. thus, when the conventional denotation is apprehended directly, without the intervention of any other agency, the word is said to be 'expressive' of the denotation or meaning. in a sentence the words also need to satisfy three @xcite conditions to be able to express the meaningful sentence. they need to have 'mutual requirement' as in all of them are needed, they need to be 'compatible' with each other and there needs to be 'proximity' meaning certain words need to be next to each other. consider the sentence "the student is studying mathematics". it is very clearly understood what each word is denoting, hence each word is expressive. moreover, they satisfy the three conditions of 'mutual requirement', 'compatibility' and 'proximity'; therefore, the sentence is a meaningful sentence. the meaning of such a sentence obtained by the meanings of the 'expressive' words is called the 'expressed' yogyatā-sannidhivaśād vakṣyamāṇasvarūpānāṃ padārthānāṃ samanvaye tātparyārtho viśeṣavapuḥ apathārthopi vākyārthaḥ when the denotations of different words become related together though 'mutual requirement', compatibility' and 'proximity' there appears in the shape of the 'meaning of the sentence' which is not expressed by any single word constituting the sentence. meaning. it can also be referred to as the 'primary' meaning of the sentence. indicative 7 : when the 'primary' (or 'expressed') meaning does not make sense (because of incompatibility), another meaning, which is in close affinity to what the word is denoting, is implied by the word. such a meaning is called the 'indicated' meaning that such a word is the 'indicative' word in the given sentence. consider the sentence "drunk by the joy of singing, i forget myself". here when we put together the 'primary' meanings of all the words we see there is incompatibility as joy is not a physical drink that one can get drunk on. the word drunk here implies the meaning overtaken or completely filled with. using this implied meaning of the word drunk, we arrive at the meaning "overjoyed by singing, i forget myself". this is called the 'indicated' meaning and the word drunk is the 'indicative' word in this sentence. this process of implying the 'indicated' meaning is called 'indication'. the 'indicated' meaning of such a sentence makes the import of the sentence much clearer and is also very easily translatable by a machine learning model to any other language suggestive: where the 'primary' meaning is clear, there can also exist a 'suggested' meaning. such a word is called the 'suggestive' word. the 'suggested' meaning can also exist along with the 'indicated' meaning. since the focus of this paper is on the 'indicated' meaning and its application, we do not go into the details of this category. ## conclusion where the cnn2d correctly identified the existence of indication, it performed very well in identifying the sub-categories of indication except in the case of the two super-imponent indications. we believe this because of the lack of equal number of examples across categories in our training dataset. the improvement in bleu score achieved for translations with paraphrasing by t5-i is significant and encouraging. the difference in the translation with and without t5-i paraphrasing was very evident in more complicated literary usages of sentences (and not just metaphors). for the purpose of the pilot we trained blackbox 15 implementations of cnn2d and t5-base in with a small dataset. we believe fine-tuning of the model architecture and a limited increase in the dataset can improve accuracy of the models for paraphrasing and translating sentences in literary works with indicated meaning to a higher level of accuracy. where word embeddings are available the trained models should also work across languages.. there are other use cases as well, where understanding the real intent of a sentence depends on understanding of the indicated meaning, including dialogue systems, sentiment analysis and emotion analysis.