Why Find the Right One? (2021)

Abstract: The present paper investigates the impact of the anaphoric one words in English on the Neural Machine Translation (NMT) process using English-Hindi as source and target language pair. As expected, the experimental results show that the state-of-the-art Google English-Hindi NMT system achieves significantly poorly on sentences containing anaphoric ones as compared to the sentences containing regular, non-anaphoric ones. But, more importantly, we note that amongst the anaphoric words, the noun class is clearly much harder for NMT than the determinatives. This reaffirms the linguistic disparity of the two phenomenon in recent theoretical syntactic literature, despite the obvious surface similarities.

## introduction english has three distinct lexemes spelled as onethe regular third person indefinite pronoun, such as in (1); the indefinite cardinal numeral (determinative), such as in (2); and regular common count noun, such as in (3). 1. one must obey the laws of the state at all times. a visible difference in their orthographic base form is not observable. however, these can be totally differentiated on the basis of their morphological, syntactic, and semantic functions in the language. note that the examples presented in (1), (2) and (3) are non-anaphoric one words. coming to the anaphoric class of onewe have two subtypes. the first one belongs to the determinative category, as seen in (4); and the second one is a noun, as in (5). 4. i bought three red glasses, but she bought only one. as expected, the determinative anaphoric ones behave like a determiner, and the one-anaphora behave like nouns in a sentence. note that the plural form of the determinative one in example (4) is some, but that of one-anaphora in (5) is ones. they are also different with respect to the kind of antecedents they take. the constituent whose repetition the determinative anaphora avoids is the whole np, a glass. but in case of one-anaphora, it is the noun head optionally with one or more of its modifiers red glass, but never the whole np @xcite . like other cohesive devices like pronouns and ellipsis, anaphoric ones make language less redundant and more engaging @xcite @xcite . resolving the information encoded in such structures is not hard for humans as they can easily disambiguate meanings from linguistic or extralinguistic context, cognitive commonsense extension as well as logical reasoning @xcite . however, all of this is not that straightforward for a machine. in fact, anaphoric ones can potentially present a special challenge for machine translation (mt) since the meaning of the word does not come from its most frequent usage as a cardinal number, but instead relies on its context, thereby becoming unavailable overtly at the surface syntax for text processing. ## previous work the determinative anaphoric ones have been discussed majorly as an instance of noun ellipsis, nominal ellipsis or noun phrase ellipsis (npe) in linguistics @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . one-anaphora, on the other hand, has been referred to as noun anaphora, oneinsertion, one-substitution and pronominalization @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . to the best of our knowledge, the earliest computational approach to one-anaphora comes from @xcite , who presents several linguisticallymotivated heuristics to distinguish one-anaphora from other non-anaphoric uses of one in english, and later from @xcite that uses gardiner's heuristics as features to train a simple machine learning (ml) model. another seminal work on the anaphoric one comes from @xcite where it has been treated as one of the several sense anaphoric relations in english. the authors create sananotes corpus where they annotate one third of the ontonotes corpus for sense anaphora. they use a support vector machine (svm) classifier -liblinear implementation @xcite along with 31 lexical and syntactic features, to distinguish between the anaphoric and the non-anaphoric class. trained and tested on one-third of the ontonotes dataset annotated as the sananotes corpus, their system achieves 61.80% f1 score on the detection of all anaphoric relations, including one-anaphora. the detection and resolution of the determinative one anaphor, on the other hand, has been carried out as a part of computational research on noun ellipsis @xcite . recent research shows that discourse devices such as pronominal anaphora, ellipsis, deixis and lexical cohesion create inconsistencies in mt output @xcite . unlike these discourse devices, however, the exact role of anaphoric ones in nlp tasks such as mt has not been studied. in the present paper, we conduct a data-driven study to study this extent and nature of this impact, using english and hindi as source and target language pairs. point fluency adequacy 4 flawless perfect/ideal 3 few errors mostly correct 2 many errors somewhat correct 1 unacceptable unrelated to source ## results and discussion as can be seen, bleu is the lower for the sentences containing anaphoric ones as compared to non-anaphoric ones. however, this may not be indicative of a trend as the test set is small and the difference observed is not that huge. coming to manual evaluation, a total of 389 sentences from the first set, 601 from the second test set and 202 sentences from the third set get a rating of either 1 or 2 in the adequacy evaluation perspective. see as compared to one-anaphora, the severity of wrong translations for determinative anaphoric ones is slightly less. hindi is morphologically richer as compared to english. we observe that the error in the translations come from copying of wrong agreement morphology on the verb in the absence of the noun whose repetition the determinative anaphoric one avoids. see figure 3 for one such example. this also implies that although such sentences get a lower rating for fluency, they rate higher for translation adequacy. from a long time in traditional syntactic literature, right from @xcite , one-anaphora and determinative anaphoric one words have been clubbed together, with frequent interchangeable uses of them in discussions and analysis. it is only recently @xcite that the morphological, syntactic and semantic differences between the two anaphoric forms have been extensively discussed. note that although recent work by @xcite aims to render all instances of the word one a homogeneous internal structure, comprising a classifier merged with an indefintive article through a variety of examples, he too identifies subtypes within this class and points out how they behave differently than one another. our simple experiment highlights the differences between these two forms, restating their linguistic analysis and advocating for a disparate treatment for them in future computational linguistics and nlp research. finally, in the sentences that are correctly translated, we observe that a majority of the oneanaphora and the determinative anaphoric ones get translated exactly into their antecedent. this means that the anaphoric expression per se is lost in the target language. for instance, the corresponding expression for one-anaphora in hindi is vaala (singular, masculine). we see only 69 out of 750 translated sentences actually containing this lexeme. it is not surprising that 66 out of such sentences are rated 4 in the evaluation. it is debatable, however, to claim that a translation that contains an anaphoric expression similar to the source is of better quality as compared to the translation that only copies the antecedent and replaces the anaphor with it. while both achieves nearly the same meaning and are grammatically acceptable, in our experiment, the former type were rated higher. it could be, then, argued that the latter added redundant information which might not be desirable in most cases. ## conclusion in the present paper, we performed a simple experiment to investigate the impact of anaphoric and non-anaphoric one words on neural machine translation process using english and hindi as source and target language pair. evaluation by manual methods revealed that anaphoric instances of the word one are much harder to translate as compared to the non-anaphoric one words. we also conclude that within the anaphoric class, oneanaphora are harder to translate than determinative anaphors, which reaffirms the linguistic disparity between the two phenomenon as shown in recent syntactic research. the long term goal of such a study is to improve the quality of translation of discourse structures such as anaphoric ones.