Weak supervision and label noise handling for Natural language processing in low-resource scenarios

The lack of large amounts of labeled data is a significant factor blocking many low-resource languages and domains from catching up with recent advancements in natural language processing. To reduce this dependency on labeled instances, weak supervision (semi-)automatically annotates unlabeled data. These labels can be obtained more quickly and cheaply than manual, gold-standard annotations. They also, however, contain more errors. Handling these noisy labels is often required to leverage the weakly supervised data successfully. In this dissertation, we study the whole weak supervision pipeline with a focus on the task of named entity recognition. We develop a tool for automatic annotation, and we propose an approach to model label noise when a small amount of clean data is available. We study the factors that influence the noise model's quality from a theoretic perspective, and we validate this approach empirically on several different tasks and languages. An important aspect is the aim for a realistic evaluation. We perform our analysis, among others, on several African low-resource languages. We show the performance benefits that can be achieved using weak supervision and label noise modeling. But we also highlight open issues that the field still has to overcome. For the low-resource settings, we expand the analysis to few-shot learning. For classification errors, we present a novel approach to obtain interpretable insights of where classifiers fail.Der Mangel an annotierten Daten ist ein wesentlicher Faktor, der viele Sprachen und Domänen mit geringen Ressourcen daran hindert, mit den jüngsten Fortschritten in der digitalen Textverarbeitung Schritt zu halten. Um diese Abhängigkeit von gelabelten Trainingsdaten zu verringern, werden bei Weak Supervision nicht gelabelte Daten (halb-)automatisch annotiert. Diese Annotationen sind schneller und günstiger zu erhalten. Sie enthalten jedoch auch mehr Fehler. Oft ist eine besondere Behandlung dieser Noisy Labels notwendig, um die Daten erfolgreich nutzen zu können. In dieser Dissertation untersuchen wir die gesamte Weak Supervision Pipeline mit einem Schwerpunkt auf den Einsatz für die Erkennung von Entitäten. Wir entwickeln ein Tool zur automatischen Annotation und präsentieren einen neuen Ansatz zur Modellierung von Noisy Labels. Wir untersuchen die Faktoren, die die Qualität dieses Modells aus theoretischer Sicht beeinflussen, und wir validieren den Ansatz empirisch für verschiedene Aufgaben und Sprachen. Ein wichtiger Aspekt dieser Arbeit ist das Ziel einer realistischen Analyse. Die Untersuchung führen wir unter anderem an mehreren afrikanischen Sprachen durch und zeigen die Leistungsvorteile, die durch Weak Supervision und die Modellierung von Label Noise erreicht werden können. Auch erweitern wir die Analyse auf das Lernen mit wenigen Beispielen. In Bezug auf Klassifizierungsfehler, stellen wir zudem einen neuen Ansatz vor, um interpretierbare Erkenntnisse zu gewinnen

Predicting Paid Certification in Massive Open Online Courses

Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

Towards Minimal Supervision BERT-Based Grammar Error Correction (Student Abstract)

Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents--or short passages--in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020

Automated Assessment of the Aftermath of Typhoons Using Social Media Texts

Disasters are one of the major threats to economics and human societies, causing substantial losses of human lives, properties and infrastructures. It has been our persistent endeavors to understand, prevent and reduce such disasters, and the popularization of social media is offering new opportunities to enhance disaster management in a crowd-sourcing approach. However, social media data is also characterized by its undue brevity, intense noise, and informality of language. The existing literature has not completely addressed these disadvantages, otherwise vast manual efforts are devoted to tackling these problems. The major focus of this research is on constructing a holistic framework to exploit social media data in typhoon damage assessment. The scope of this research covers data collection, relevance classification, location extraction and damage assessment while assorted approaches are utilized to overcome the disadvantages of social media data. Moreover, a semi-supervised or unsupervised approach is prioritized in forming the framework to minimize manual intervention. In data collection, query expansion strategy is adopted to optimize the search recall of typhoon-relevant information retrieval. Multiple filtering strategies are developed to screen the keywords and maintain the relevance to search topics in the keyword updates. A classifier based on a convolutional neural network is presented for relevance classification, with hashtags and word clusters as extra input channels to augment the information. In location extraction, a model is constructed by integrating Bidirectional Long Short-Time Memory and Conditional Random Fields. Feature noise correction layers and label smoothing are leveraged to handle the noisy training data. Finally, a multi-instance multi-label classifier identifies the damage relations in four categories, and the damage categories of a message are integrated with the damage descriptions score to obtain damage severity score for the message. A case study is conducted to verify the effectiveness of the framework. The outcomes indicate that the approaches and models developed in this study significantly improve in the classification of social media texts especially under the framework of semi-supervised or unsupervised learning. Moreover, the results of damage assessment from social media data are remarkably consistent with the official statistics, which demonstrates the practicality of the proposed damage scoring scheme

Text generation for small data regimes

In Natural Language Processing (NLP), applications trained on downstream tasks for text classification usually require enormous amounts of data to perform well. Neural Network (NN) models are among the applications that can always be trained to produce better results. Yet, a huge factor in improving results is the ability to scale over large datasets. Given that Deep NNs are known to be data hungry, having more training samples can always be beneficial. For a classification model to perform well, it could require thousands or even millions of textual training examples. Transfer learning enables us to leverage knowledge gained from general data collections to perform well on target tasks. In NLP, training language models on large data collections has been shown to achieve great results when tuned to different task-specific datasets Wang et al. (2019, 2018a). However, even with transfer learning, adequate training data remains a condition for training machine learning models. Nonetheless, we show that small textual datasets can be augmented to a degree that is enough to achieve improved classification performance. In this thesis, we make multiple contributions to data augmentation. Firstly, we transform the data generation task into an optimization problem which maximizes the usefulness of the generated output, using Monte Carlo Tree Search (MCTS) as the optimization strategy and incorporating entropy as one of the optimization criteria. Secondly, we propose a language generation approach for targeted data generation with the participation of the training classifier. With a user in the loop, we find that manual annotation of a small proportion of the generated data is enough to boost classification performance. Thirdly, under a self-learning scheme, we replace the user by an automated approach in which the classifier is trained on its own pseudo-labels. Finally, we extend the data generation approach to the knowledge distillation domain, by generating samples that a teacher model can confidently label, but not its student