Kvaliteedi hindamine tähelepanu abil

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMasintõlge on saanud osaks mitte ainult keeleteadlaste ja professionaalsete tõlkijate, vaid peaaegu kõigi elust. Enamik inimesi, kes on kasutanud masintõlget, on kohanud naljakaid ja kohati täiesti valesid tõlkeid, mis lause tähendust täielikult moonutavad. Seega peame peale masintõlke mudeli kasutama hindamismehhanismi, mis teavitab inimesi tõlgete kvaliteedist. Loomulikult saavad professionaalsed tõlkijad masintõlke väljundit hinnata ja vajadusel toimetada. Inimeste märkuste kasutamine veebipõhiste masintõlkesüsteemide tõlgete hindamiseks on aga äärmiselt kulukas ja ebapraktiline. Seetõttu on automatiseeritud tõlkekvaliteedi hindamise süsteemid masintõlke töövoo oluline osa. Kvaliteedihinnangu eesmärk on ennustada masintõlke väljundi kvaliteeti, ilma etalontõlgeteta. Selles töös keskendusime kvaliteedihinnangu mõõdikutele ja käsitleme tõlkekvaliteedi näitajana tähelepanumehhanismi ennustatud jaotusi, mis on üks kaasaegsete neuromasintõlke (NMT) süsteemide sisemistest parameetritest. Kõigepealt rakendasime seda rekurrentsetel närvivõrkudel (RNN) põhinevatele masintõlkemudelitele ja analüüsisime pakutud meetodite toimivust juhendamata ja juhendatud ülesannete jaoks. Kuna RNN-põhised MT-süsteemid on nüüdseks asendunud transformeritega, mis muutusid peamiseks tipptaseme masintõlke tehnoloogiaks, kohandasime oma lähenemisviisi ka transformeri arhitektuurile. Näitasime, et tähelepanupõhised meetodid sobivad nii juhendatud kui ka juhendamata ülesannete jaoks, kuigi teatud piirangutega. Kuna annotatsiooni andmete hankimine on üsna kulukas, uurisime, kui palju annoteeritud andmeid on vaja kvaliteedihinnangu mudeli treenimiseks.Machine translation has become a part of the life of not only linguists and professional translators, but almost everyone. Most people who have used machine translation have come across funny and sometimes completely incorrect translations that turn the meaning of a sentence upside down. Thus, apart from a machine translation model, we need to use a scoring mechanism that informs people about the quality of translations. Of course, professional translators can assess and, if necessary, edit the machine translation output. However, using human annotations to evaluate translations of online machine translation systems is extremely expensive and impractical. That is why automated systems for measuring translation quality are a crucial part of the machine translation pipeline. Quality Estimation aims to predict the quality of machine translation output at run-time without using any gold-standard human annotations. In this work, we focused on Quality Estimation methods and explored the distribution of attention—one of the internal parameters of modern neural machine translation systems—as an indicator of translation quality. We first applied it to machine translation models based on recurrent neural networks (RNNs) and analyzed the performance of proposed methods for unsupervised and supervised tasks. Since transformer-based machine translation models had supplanted RNN-based, we adapted our approach to the attention extracted from transformers. We demonstrated that attention-based methods are suitable for both supervised and unsupervised tasks, albeit with some limitations. Since getting annotation labels is quite expensive, we looked at how much annotated data is needed to train a quality estimation model.https://www.ester.ee/record=b549935

Injecting Inductive Biases into Distributed Representations of Text

Distributed real-valued vector representations of text (a.k.a. embeddings), learned by neural networks, encode various (linguistic) knowledge. To encode this knowledge into the embeddings the common approach is to train a large neural network on large corpora. There is, however, a growing concern regarding the sustainability and rationality of pursuing this approach further. We depart from the mainstream trend and instead, to incorporate the desired properties into embeddings, use inductive biases. First, we use Knowledge Graphs (KGs) as a data-based inductive bias to derive the semantic representation of words and sentences. The explicit semantics that is encoded in a structure of a KG allows us to acquire the semantic representations without the need of employing a large amount of text. We use graph embedding techniques to learn the semantic representation of words and the sequence-to-sequence model to learn the semantic representation of sentences. We demonstrate the efficacy of the inductive bias for learning embeddings for rare words and the ability of sentence embeddings to encode topological dependencies that exist between entities of a KG. Then, we explore the amount of information and sparsity as two key (data-agnostic) inductive biases to regulate the utilisation of the representation space. We impose these properties with Variational Autoencoders (VAEs). First, we regulate the amount of information encoded in a sentence embedding via constraint optimisation of a VAE objective function. We show that increasing amount of information allows to better discriminate sentences. Afterwards, to impose distributed sparsity we design a state-of-the-art Hierarchical Sparse VAE with a flexible posterior which captures the statistical characteristics of text effectively. While sparsity, in general, has desired computational and statistical representational properties, it is known to compensate task performance. We illustrate that with distributed sparsity, task performance could be maintained or even improved. The findings of the thesis advocate further development of inductive biases that could mitigate the dependence of representation learning quality on large data and model sizes

Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks

Recently, there has been growth in providers of speech transcription services enabling others to leverage technology they would not normally be able to use. As a result, speech-enabled solutions have become commonplace. Their success critically relies on the quality, accuracy, and reliability of the underlying speech transcription systems. Those black box systems, however, offer limited means for quality control as only word sequences are typically available. This paper examines this limited resource scenario for confidence estimation, a measure commonly used to assess transcription reliability. In particular, it explores what other sources of word and sub-word level information available in the transcription process could be used to improve confidence scores. To encode all such information this paper extends lattice recurrent neural networks to handle sub-words. Experimental results using the IARPA OpenKWS 2016 evaluation system show that the use of additional information yields significant gains in confidence estimation accuracy. The implementation for this model can be found online.Comment: 5 pages, 8 figures, ICASSP submissio

The Zero Resource Speech Challenge 2017

We describe a new challenge aimed at discovering subword and word units from raw speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It aims at constructing systems that generalize across languages and adapt to new speakers. The design features and evaluation metrics of the challenge are presented and the results of seventeen models are discussed.Comment: IEEE ASRU (Automatic Speech Recognition and Understanding) 2017. Okinawa, Japa

Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialised cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialised vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.Ivan Vulic, Roi Reichart and Anna Korhonen are supported by the ERC Consolidator Grant LEXICAL (number 648909). Roi Reichart is also supported by the Intel-ICRI grant: Hybrid Models for Minimally Supervised Information Extraction from Conversations

Automatic information search for countering covid-19 misinformation through semantic similarity

Trabajo Fin de Máster en Bioinformática y Biología ComputacionalInformation quality in social media is an increasingly important issue and misinformation problem has become even more critical in the current COVID-19 pandemic, leading people exposed to false and potentially harmful claims and rumours. Civil society organizations, such as the World Health Organization, have demanded a global call for action to promote access to health information and mitigate harm from health misinformation. Consequently, this project pursues countering the spread of COVID-19 infodemic and its potential health hazards. In this work, we give an overall view of models and methods that have been employed in the NLP field from its foundations to the latest state-of-the-art approaches. Focusing on deep learning methods, we propose applying multilingual Transformer models based on siamese networks, also called bi-encoders, combined with ensemble and PCA dimensionality reduction techniques. The goal is to counter COVID-19 misinformation by analyzing the semantic similarity between a claim and tweets from a collection gathered from official fact-checkers verified by the International Fact-Checking Network of the Poynter Institute. It is factual that the number of Internet users increases every year and the language spoken determines access to information online. For this reason, we give a special effort in the application of multilingual models to tackle misinformation across the globe. Regarding semantic similarity, we firstly evaluate these multilingual ensemble models and improve the result in the STS-Benchmark compared to monolingual and single models. Secondly, we enhance the interpretability of the models’ performance through the SentEval toolkit. Lastly, we compare these models’ performance against biomedical models in TREC-COVID task round 1 using the BM25 Okapi ranking method as the baseline. Moreover, we are interested in understanding the ins and outs of misinformation. For that purpose, we extend interpretability using machine learning and deep learning approaches for sentiment analysis and topic modelling. Finally, we developed a dashboard to ease visualization of the results. In our view, the results obtained in this project constitute an excellent initial step toward incorporating multilingualism and will assist researchers and people in countering COVID-19 misinformation