Crosslingual Document Embedding as Reduced-Rank Ridge Regression

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word vectors, postprocess them to make them crosslingual, and finally average word vectors to obtain document vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as document-level. Moreover, since our algorithm uses the singular value decomposition as its core operation, it is highly scalable. Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task. Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19

Sentence Alignment using MR and GA

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on mathematical regression (MR) and genetic algorithm (GA) classifiers are presented. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the mathematical regression and genetic algorithm models. Another set of data was used for testing. The results of (MR) and (GA) outperform the results of length based approach. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research

Distributional semantics and machine learning for statistical machine translation

[EU]Lan honetan semantika distribuzionalaren eta ikasketa automatikoaren erabilera aztertzen dugu itzulpen automatiko estatistikoa hobetzeko. Bide horretan, erregresio logistikoan oinarritutako ikasketa automatikoko eredu bat proposatzen dugu hitz-segiden itzulpen- probabilitatea modu dinamikoan modelatzeko. Proposatutako eredua itzulpen automatiko estatistikoko ohiko itzulpen-probabilitateen orokortze bat dela frogatzen dugu, eta testuinguruko nahiz semantika distribuzionaleko informazioa barneratzeko baliatu ezaugarri lexiko, hitz-cluster eta hitzen errepresentazio bektorialen bidez. Horretaz gain, semantika distribuzionaleko ezagutza itzulpen automatiko estatistikoan txertatzeko beste hurbilpen bat lantzen dugu: hitzen errepresentazio bektorial elebidunak erabiltzea hitz-segiden itzulpenen antzekotasuna modelatzeko. Gure esperimentuek proposatutako ereduen baliagarritasuna erakusten dute, emaitza itxaropentsuak eskuratuz oinarrizko sistema sendo baten gainean. Era berean, gure lanak ekarpen garrantzitsuak egiten ditu errepresentazio bektorialen mapaketa elebidunei eta hitzen errepresentazio bektorialetan oinarritutako hitz-segiden antzekotasun neurriei dagokienean, itzulpen automatikoaz haratago balio propio bat dutenak semantika distribuzionalaren arloan.[EN]In this work, we explore the use of distributional semantics and machine learning to improve statistical machine translation. For that purpose, we propose the use of a logistic regression based machine learning model for dynamic phrase translation probability mod- eling. We prove that the proposed model can be seen as a generalization of the standard translation probabilities used in statistical machine translation, and use it to incorporate context and distributional semantic information through lexical, word cluster and word embedding features. Apart from that, we explore the use of word embeddings for phrase translation probability scoring as an alternative approach to incorporate distributional semantic knowledge into statistical machine translation. Our experiments show the effectiveness of the proposed models, achieving promising results over a strong baseline. At the same time, our work makes important contributions in relation to bilingual word embedding mappings and word embedding based phrase similarity measures, which go be- yond machine translation and have an intrinsic value in the field of distributional semantics

Unveiling Biases in Word Embeddings: An Algorithmic Approach for Comparative Analysis Based on Alignment

openWord embeddings are state-of-the-art vectorial representation of words with the goal of preserving semantic similarity. They are the result of specific learning algorithms trained on usually large corpora. Consequently, they inherit all biases of the corpora on which they have been trained on. The goal of the thesis is to devise and adapt an efficient algorithm to compare two different word embeddings in order to highlight the biases they are subjected to. Specifically, we look for an alignment between the two vector spaces, corresponding to the two word embeddings, that minimises the difference between the stable words, i.e. the ones that have not changed in the two embeddings, thus highlighting the differences between the ones that did changed. In this work, we test this idea adapting a machine translation framework called MUSE that, after some improvements, can run over multiple cores in a HPC framework, specifically managed with SLURM. We also provide an amplpy implementation of linear and convex programming algorithms adapted to our case. We then test these techniques on a corpus of text taken from Italian newspapers in order to identify which words are more subject to change among the different pairs of corpora.Word embeddings are state-of-the-art vectorial representation of words with the goal of preserving semantic similarity. They are the result of specific learning algorithms trained on usually large corpora. Consequently, they inherit all biases of the corpora on which they have been trained on. The goal of the thesis is to devise and adapt an efficient algorithm to compare two different word embeddings in order to highlight the biases they are subjected to. Specifically, we look for an alignment between the two vector spaces, corresponding to the two word embeddings, that minimises the difference between the stable words, i.e. the ones that have not changed in the two embeddings, thus highlighting the differences between the ones that did changed. In this work, we test this idea adapting a machine translation framework called MUSE that, after some improvements, can run over multiple cores in a HPC framework, specifically managed with SLURM. We also provide an amplpy implementation of linear and convex programming algorithms adapted to our case. We then test these techniques on a corpus of text taken from Italian newspapers in order to identify which words are more subject to change among the different pairs of corpora

A Deep Network Model for Paraphrase Detection in Short Text Messages

This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus