Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models

This dissertation focuses on effective combination of data-driven natural language processing (NLP) approaches with linguistic knowledge sources that are based on manual text annotation or word grouping according to semantic commonalities. I gainfully apply fine-grained linguistic soft constraints -- of syntactic or semantic nature -- on statistical NLP models, evaluated in end-to-end state-of-the-art statistical machine translation (SMT) systems. The introduction of semantic soft constraints involves intrinsic evaluation on word-pair similarity ranking tasks, extension from words to phrases, application in a novel distributional paraphrase generation technique, and an introduction of a generalized framework of which these soft semantic and syntactic constraints can be viewed as instances, and in which they can be potentially combined. Fine granularity is key in the successful combination of these soft constraints, in many cases. I show how to softly constrain SMT models by adding fine-grained weighted features, each preferring translation of only a specific syntactic constituent. Previous attempts using coarse-grained features yielded negative results. I also show how to softly constrain corpus-based semantic models of words (“distributional profiles”) to effectively create word-sense-aware models, by using semantic word grouping information found in a manually compiled thesaurus. Previous attempts, using hard constraints and resulting in aggregated, coarse-grained models, yielded lower gains. A novel paraphrase generation technique incorporating these soft semantic constraints is then also evaluated in a SMT system. This paraphrasing technique is based on the Distributional Hypothesis. The main advantage of this novel technique over current “pivoting” techniques for paraphrasing is the independence from parallel texts, which are a limited resource. The evaluation is done by augmenting translation models with paraphrase-based translation rules, where fine-grained scoring of paraphrase-based rules yields significantly higher gains. The model augmentation includes a novel semantic reinforcement component: In many cases there are alternative paths of generating a paraphrase-based translation rule. Each of these paths reinforces a dedicated score for the “goodness” of the new translation rule. This augmented score is then used as a soft constraint, in a weighted log-linear feature, letting the translation model learn how much to “trust” the paraphrase-based translation rules. The work reported here is the first to use distributional semantic similarity measures to improve performance of an end-to-end phrase-based SMT system. The unified framework for statistical NLP models with soft linguistic constraints enables, in principle, the combination of both semantic and syntactic constraints -- and potentially other constraints, too -- in a single SMT model

Augmenting Translation Lexica by Learning Generalised Translation Patterns

Bilingual Lexicons do improve quality: of parallel corpora alignment, of newly extracted translation pairs, of Machine Translation, of cross language information retrieval, among other applications. In this regard, the first problem addressed in this thesis pertains to the classification of automatically extracted translations from parallel corpora-collections of sentence pairs that are translations of each other. The second problem is concerned with machine learning of bilingual morphology with applications in the solution of first problem and in the generation of Out-Of-Vocabulary translations. With respect to the problem of translation classification, two separate classifiers for handling multi-word and word-to-word translations are trained, using previously extracted and manually classified translation pairs as correct or incorrect. Several insights are useful for distinguishing the adequate multi-word candidates from those that are inadequate such as, lack or presence of parallelism, spurious terms at translation ends such as determiners, co-ordinated conjunctions, properties such as orthographic similarity between translations, the occurrence and co-occurrence frequency of the translation pairs. Morphological coverage reflecting stem and suffix agreements are explored as key features in classifying word-to-word translations. Given that the evaluation of extracted translation equivalents depends heavily on the human evaluator, incorporation of an automated filter for appropriate and inappropriate translation pairs prior to human evaluation contributes to tremendously reduce this work, thereby saving the time involved and progressively improving alignment and extraction quality. It can also be applied to filtering of translation tables used for training machine translation engines, and to detect bad translation choices made by translation engines, thus enabling significative productivity enhancements in the post-edition process of machine made translations. An important attribute of the translation lexicon is the coverage it provides. Learning suffixes and suffixation operations from the lexicon or corpus of a language is an extensively researched task to tackle out-of-vocabulary terms. However, beyond mere words or word forms are the translations and their variants, a powerful source of information for automatic structural analysis, which is explored from the perspective of improving word-to-word translation coverage and constitutes the second part of this thesis. In this context, as a phase prior to the suggestion of out-of-vocabulary bilingual lexicon entries, an approach to automatically induce segmentation and learn bilingual morph-like units by identifying and pairing word stems and suffixes is proposed, using the bilingual corpus of translations automatically extracted from aligned parallel corpora, manually validated or automatically classified. Minimally supervised technique is proposed to enable bilingual morphology learning for language pairs whose bilingual lexicons are highly defective in what concerns word-to-word translations representing inflection diversity. Apart from the above mentioned applications in the classification of machine extracted translations and in the generation of Out-Of-Vocabulary translations, learned bilingual morph-units may also have a great impact on the establishment of correspondences of sub-word constituents in the cases of word-to-multi-word and multi-word-to-multi-word translations and in compression, full text indexing and retrieval applications

Knowledge Graphs Effectiveness in Neural Machine Translation Improvement

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)---assigning a unique identity to entities---to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language to be carried over to the corresponding entities in the target-language translation. The method is evaluated for English-Spanish translation exploiting Freebase as a source of knowledge. Our experimental results show that exploiting KG information not only decreases the number of unknown words in the translation but also improves translation quality

The Circle of Meaning: From Translation to Paraphrasing and Back

The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection

Using Comparable Corpora to Augment Statistical Machine Translation Models in Low Resource Settings

Previously, statistical machine translation (SMT) models have been estimated from parallel corpora, or pairs of translated sentences. In this thesis, we directly incorporate comparable corpora into the estimation of end-to-end SMT models. In contrast to parallel corpora, comparable corpora are pairs of monolingual corpora that have some cross-lingual similarities, for example topic or publication date, but that do not necessarily contain any direct translations. Comparable corpora are more readily available in large quantities than parallel corpora, which require significant human effort to compile. We use comparable corpora to estimate machine translation model parameters and show that doing so improves performance in settings where a limited amount of parallel data is available for training. The major contributions of this thesis are the following: * We release ‘language packs’ for 151 human languages, which include bilingual dictionaries, comparable corpora of Wikipedia document pairs, comparable corpora of time-stamped news text that we harvested from the web, and, for non-roman script languages, dictionaries of name pairs, which are likely to be transliterations. * We present a novel technique for using a small number of example word translations to learn a supervised model for bilingual lexicon induction which takes advantage of a wide variety of signals of translation equivalence that can be estimated over comparable corpora. * We show that using comparable corpora to induce new translations and estimate new phrase table feature functions improves end-to-end statistical machine translation performance for low resource language pairs as well as domains. * We present a novel algorithm for composing multiword phrase translations from multiple unigram translations and then use comparable corpora to prune the large space of hypothesis translations. We show that these induced phrase translations improve machine translation performance beyond that of component unigrams. This thesis focuses on critical low resource machine translation settings, where insufficient parallel corpora exist for training statistical models. We experiment with both low resource language pairs and low resource domains of text. We present results from our novel error analysis methodology, which show that most translation errors in low resource settings are due to unseen source language words and phrases and unseen target language translations. We also find room for fixing errors due to how different translations are weighted, or scored, in the models. We target both error types; we use comparable corpora to induce new word and phrase translations and estimate novel translation feature scores. Our experiments show that augmenting baseline SMT systems with new translations and features estimated over comparable corpora improves translation performance significantly. Additionally, our techniques expand the applicability of statistical machine translation to those language pairs for which zero parallel text is available

A study of the translation of sentiment in user-generated text

A thesis submitted in partial ful filment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Emotions are biological states of feeling that humans may verbally express to communicate their negative or positive mood, influence others, or even afflict harm. Although emotions such as anger, happiness, affection, or fear are supposedly universal experiences, the lingual realisation of the emotional experience may vary in subtle ways across different languages. For this reason, preserving the original sentiment of the source text has always been a challenging task that draws in a translator's competence and fi nesse. In the professional translation industry, an incorrect translation of the sentiment-carrying lexicon is considered a critical error as it can be either misleading or in some cases harmful since it misses the fundamental aspect of the source text, i.e. the author's sentiment. Since the advent of Neural Machine Translation (NMT), there has been a tremendous improvement in the quality of automatic translation. This has lead to an extensive use of NMT online tools to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards an entity. In such scenarios, the process of translating the user's sentiment is entirely automatic with no human intervention, neither for post-editing nor for accuracy checking. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes critical translation errors that may not only distort the sentiment but at times flips the polarity of the source text to its exact opposite. In this thesis, we tackle the translation of sentiment in UGT by NMT systems from two perspectives: analytical and experimental. First, the analytical approach introduces a list of linguistic features that can lead to a mistranslation of ne-grained emotions between different language pairs in the UGT domain. It also presents an error-typology specifi c to Arabic UGT illustrating the main linguistic phenomena that can cause mistranslation of sentiment polarity when translating Arabic UGT into English by NMT systems. Second, the experimental approach attempts to improve the translation of sentiment by addressing some of the linguistic challenges identifi ed in the analysis as causing mistranslation of sentiment both on the word-level and on the sentence-level. On the word-level, we propose a Transformer NMT model trained on a sentiment-oriented vector space model (VSM) of UGT data that is capable of translating the correct sentiment polarity of challenging contronyms. On the sentence-level, we propose a semi-supervised approach to overcome the problem of translating sentiment expressed by dialectical language in UGT data. We take the translation of dialectical Arabic UGT into English as a case study. Our semi-supervised AR-EN NMT model shows improved performance over the online MT Twitter tool in translating dialectical Arabic UGT not only in terms of translation quality but also in the preservation of the sentiment polarity of the source text. The experimental section also presents an empirical method to quantify the notion of sentiment transfer by an MT system and, more concretely, to modify automatic metrics such that its MT ranking comes closer to a human judgement of a poor or good translation of sentiment

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding