Do we really need fully unsupervised cross-lingual embeddings?

Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero BLI performance for 87/210 pairs). Even when they succeed, they never surpass the performance of weakly supervised methods (seeded with 500-1,000 translation pairs) using the same self-learning procedure in any BLI setup, and the gaps are often substantial. These findings call for revisiting the main motivations behind fully unsupervised CLWE methods

On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation

Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual similarity. In this paper, we concern ourselves with reference-free machine translation (MT) evaluation where we directly compare source texts to (sometimes low-quality) system translations, which represents a natural adversarial setup for multilingual encoders. Reference-free evaluation holds the promise of web-scale comparison of MT systems. We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER. We find that they perform poorly as semantic encoders for reference-free MT evaluation and identify their two key limitations, namely, (a) a semantic mismatch between representations of mutual translations and, more prominently, (b) the inability to punish "translationese", i.e., low-quality literal translations. We propose two partial remedies: (1) post-hoc re-alignment of the vector spaces and (2) coupling of semantic-similarity based metrics with target-side language modeling. In segment-level MT evaluation, our best metric surpasses reference-based BLEU by 5.7 correlation points.Comment: ACL2020 Camera Ready (v3: several small fixes, e.g., Unicode errors

A survey of cross-lingual word embedding models

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.</jats:p

Multi-view Representation Learning for Unifying Languages, Knowledge and Vision

The growth of content on the web has raised various challenges, yet also provided numerous opportunities. Content exists in varied forms such as text appearing in different languages, entity-relationship graph represented as structured knowledge and as a visual embodiment like images/videos. They are often referred to as modalities. In many instances, the different amalgamation of modalities co-exists to complement each other or to provide consensus. Thus making the content either heterogeneous or homogeneous. Having an additional point of view for each instance in the content is beneficial for data-driven learning and intelligent content processing. However, despite having availability of such content. Most advancements made in data-driven learning (i.e., machine learning) is by solving tasks separately for the single modality. The similar endeavor was not shown for the challenges which required input either from all or subset of them. In this dissertation, we develop models and techniques that can leverage multiple views of heterogeneous or homogeneous content and build a shared representation for aiding several applications which require a combination of modalities mentioned above. In particular, we aim to address applications such as content-based search, categorization, and generation by providing several novel contributions. First, we develop models for heterogeneous content by jointly modeling diverse representations emerging from two views depicting text and image by learning their correlation. To be specific, modeling such correlation is helpful to retrieve cross-modal content. Second, we replace the heterogeneous content with homogeneous to learn a common space representation for content categorization across languages. Furthermore, we develop models that take input from both homogeneous and heterogeneous content to facilitate the construction of common space representation from more than two views. Specifically, representation is used to generate one view from another. Lastly, we describe a model that can handle missing views, and demonstrate that the model can generate missing views by utilizing external knowledge. We argue that techniques the models leverage internally provide many practical benefits and lot of immediate value applications. From the modeling perspective, our contributed model design in this thesis can be summarized under the phrase Multi-view Representation Learning( MVRL ). These models are variations and extensions of shallow statistical and deep neural networks approaches that can jointly optimize and exploit all views of the input content arising from different independent representations. We show that our models advance state of the art, but not limited to tasks such as cross-modal retrieval, cross-language text classification, image-caption generation in multiple languages and caption generation for images containing unseen visual object categories

Acquiring and Harnessing Verb Knowledge for Multilingual Natural Language Processing

Advances in representation learning have enabled natural language processing models to derive non-negligible linguistic information directly from text corpora in an unsupervised fashion. However, this signal is underused in downstream tasks, where they tend to fall back on superficial cues and heuristics to solve the problem at hand. Further progress relies on identifying and filling the gaps in linguistic knowledge captured in their parameters. The objective of this thesis is to address these challenges focusing on the issues of resource scarcity, interpretability, and lexical knowledge injection, with an emphasis on the category of verbs. To this end, I propose a novel paradigm for efficient acquisition of lexical knowledge leveraging native speakers’ intuitions about verb meaning to support development and downstream performance of NLP models across languages. First, I investigate the potential of acquiring semantic verb classes from non-experts through manual clustering. This subsequently informs the development of a two-phase semantic dataset creation methodology, which combines semantic clustering with fine-grained semantic similarity judgments collected through spatial arrangements of lexical stimuli. The method is tested on English and then applied to a typologically diverse sample of languages to produce the first large-scale multilingual verb dataset of this kind. I demonstrate its utility as a diagnostic tool by carrying out a comprehensive evaluation of state-of-the-art NLP models, probing representation quality across languages and domains of verb meaning, and shedding light on their deficiencies. Subsequently, I directly address these shortcomings by injecting lexical knowledge into large pretrained language models. I demonstrate that external manually curated information about verbs’ lexical properties can support data-driven models in tasks where accurate verb processing is key. Moreover, I examine the potential of extending these benefits from resource-rich to resource-poor languages through translation-based transfer. The results emphasise the usefulness of human-generated lexical knowledge in supporting NLP models and suggest that time-efficient construction of lexicons similar to those developed in this work, especially in under-resourced languages, can play an important role in boosting their linguistic capacity.ESRC Doctoral Fellowship [ES/J500033/1], ERC Consolidator Grant LEXICAL [648909

Data Scarcity in Event Analysis and Abusive Language Detection

Lack of data is almost always the cause of the suboptimal performance of neural networks. Even though data scarce scenarios can be simulated for any task by assuming limited access to training data, we study two problem areas where data scarcity is a practical challenge: event analysis and abusive content detection} Journalists, social scientists and political scientists need to retrieve and analyze event mentions in unstructured text to compute useful statistical information to understand society. We claim that it is hard to specify information need about events using keyword-based representation and propose a Query by Example (QBE) setting for event retrieval. In the QBE setting, we assume that there are a few example sentences mentioning the event class a user is interested in and we aim to retrieve relevant events using only the examples as a query. Traditional event detection approaches are not applicable in this setting as event detection datasets are constructed based on pre-defined schemas which limits them to a small set of event and event-argument types. Moreover, the amount of annotated data in event detection datasets is limited that only allows us to build a retrieval corpus for evaluation. Thus we assume that there are no relevance judgments to train an event retrieval model -- except for the few examples of a specific event type. We create three QBE evaluation settings from three event detection datasets: PoliceKilling, ACE, and IndiaPoliceEvents. For the PoliceKilling dataset, where a relevant sentence describes a police killing event, we show that a query model constructed from the NLP features extracted from the few given examples is effective compared to event detection baselines. For the ACE dataset, where there are thirty-three types of events, we construct a QBE setting for each type and show that a sentence embedding approach effectively transfers for event matching. Finally, we conducted a unified evaluation of all three datasets using the sentence-embedding-based model and showed that it outperforms strong baselines. We further examine the effect of data scarcity in abusive language detection. We first study a specific type of abusive language -- hate speech. Neural hate speech detection models trained from one dataset poorly generalize to another dataset from a different domain. This is because characteristics of hate speech vary based on racial and cultural aspects. Our data scarcity scenario assumes that we have a hate speech dataset from a domain and it needs to generalize to a test set from another domain using the unlabeled data from the test domain only. Thus we assume zero target domain data in this scenario. To tackle the data scarcity, we propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs, and BERT) on three different collections. We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision. Finally, we examine the cross-lingual abusive language detection problem. Abusive language is a superclass of hate speech that includes profanity, aggression, offensiveness, cyberbullying, toxicity, and hate speech itself. There is a large collection of abusive language detection datasets in English such as Jigsaw. For other languages there exist datasets for abusive language detection but with very limited data. We propose a cross-lingual transfer learning approach to learn an effective neural abusive language classifier for such low-resource languages with help from a dataset from a resource-rich language. The framework is based on a nearest-neighbor architecture and is thus interpretable by design. It is a modern instantiation of the classic k-nearest neighbor model, as we use transformer representations in all its components. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query-neighbor interactions. We propose two encoding schemes and show their effectiveness using both qualitative and quantitative analyses. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements in F1 over strong baselines

Self-supervised learning in natural language processing

Most natural language processing (NLP) learning algorithms require labeled data. While this is given for a select number of (mostly English) tasks, the availability of labeled data is sparse or non-existent for the vast majority of use-cases. To alleviate this, unsupervised learning and a wide array of data augmentation techniques have been developed (Hedderich et al., 2021a). However, unsupervised learning often requires massive amounts of unlabeled data and also fails to perform in difficult (low-resource) data settings, i.e., if there is an increased distance between the source and target data distributions (Kim et al., 2020). This distributional distance can be the case if there is a domain drift or large linguistic distance between the source and target data. Unsupervised learning in itself does not exploit the highly informative (labeled) supervisory signals hidden in unlabeled data. In this dissertation, we show that by combining the right unsupervised auxiliary task (e.g., sentence pair extraction) with an appropriate primary task (e.g., machine translation), self-supervised learning can exploit these hidden supervisory signals more efficiently than purely unsupervised approaches, while functioning on less labeled data than supervised approaches. Our self-supervised learning approach can be used to learn NLP tasks in an efficient manner, even when the amount of training data is sparse or the data comes with strong differences in its underlying distribution, e.g., stemming from unrelated languages. For our general approach, we applied unsupervised learning as an auxiliary task to learn a supervised primary task. Concretely, we have focused on the auxiliary task of sentence pair extraction for sequence-to-sequence primary tasks (i.e., machine translation and style transfer) as well as language modeling, clustering, subspace learning and knowledge integration for primary classification tasks (i.e., hate speech detection and sentiment analysis). For sequence-to-sequence tasks, we show that self-supervised neural machine translation (NMT) achieves competitive results on high-resource language pairs in comparison to unsupervised NMT while requiring less data. Further combining self-supervised NMT with unsupervised NMT-inspired augmentation techniques makes the learning of low-resource (similar, distant and unrelated) language pairs possible. Further, using our self-supervised approach, we show how style transfer can be learned without the need for parallel data, generating stylistic rephrasings of highest overall performance on all tested tasks. For sequence-to-label tasks, we underline the benefit of auxiliary task-based augmentation over primary task augmentation. An auxiliary task that showed to be especially beneficial to the primary task performance was subspace learning, which led to impressive gains in (cross-lingual) zero-shot classification performance on similar or distant target tasks, also on similar, distant and unrelated languages.Die meisten Lernalgorithmen der Computerlingistik (CL) benötigen gelabelte Daten. Diese sind zwar für eine Auswahl an (hautpsächlich Englischen) Aufgaben verfügbar, für den Großteil aller Anwendungsfälle sind gelabelte Daten jedoch nur spärrlich bis gar nicht vorhanden. Um dem gegenzusteuern, wurde eine große Auswahl an Techniken entwickelt, welche sich das unüberwachte Lernen oder Datenaugmentierung zu eigen machen (Hedderich et al., 2021a). Unüberwachtes Lernen benötigt jedoch massive Mengen an ungelabelten Daten und versagt, wenn es mit schwierigen (resourcenarmen) Datensituationen konfrontiert wird, d.h. wenn eine größere Distanz zwischen der Quellen- und Zieldatendistributionen vorhanden ist (Kim et al., 2020). Eine distributionelle Distanz kann zum Beispiel der Fall sein, wenn ein Domänenunterschied oder eine größere sprachliche Distanz zwischen der Quellenund Zieldaten besteht. Unüberwachtes Lernen selbst nutzt die hochinformativen (gelabelten) Überwachungssignale, welche sich in ungelabelte Daten verstecken, nicht aus. In dieser Dissertation zeigen wir, dass selbstüberwachtes Lernen, durch die Kombination der richtigen unüberwachten Hilfsaufgabe (z.B. Satzpaarextraktion) mit einer passenden Hauptaufgabe (z.B. maschinelle Übersetzung), diese versteckten Überwachsungssignale effizienter ausnutzen kann als pure unüberwachte Lernalgorithmen, und dabei auch noch weniger gelabelte Daten benötigen als überwachte Lernalgorithmen. Unser selbstüberwachter Lernansatz erlaubt es uns, CL Aufgaben effizient zu lernen, selbst wenn die Trainingsdatenmenge spärrlich ist oder die Daten mit starken distributionellen Differenzen einher gehen, z.B. weil die Daten von zwei nicht verwandten Sprachen stammen. Im Generellen haben wir unüberwachtes Lernen als Hilfsaufgabe angewandt um eine überwachte Hauptaufgabe zu erlernen. Konkret haben wir uns auf Satzpaarextraktion als Hilfsaufgabe für Sequenz-zu-Sequenz Hauptaufgaben (z.B. maschinelle Übersetzung und Stilübertragung) konzentriert sowohl als auch Sprachmodelierung, Clustern, Teilraumlernen und Wissensintegration zum erlernen von Klassifikationsaufgaben (z.B. Hassredenidentifikation und Sentimentanalyse). Für Sequenz-zu-Sequenz Aufgaben zeigen wir, dass selbstüberwachte maschinelle Übersetzung (MÜ) im Vergleich zur unüberwachten MÜ wettbewerbsfähige Ergebnisse auf resourcenreichen Sprachpaaren erreicht und währenddessen weniger Daten zum Lernen benötigt. Wenn selbstüberwachte MÜ mit Augmentationstechniken, inspiriert durch unüberwachte MÜ, kombiniert wird, wird auch das Lernen von resourcenarmen (ähnlichen, entfernt verwandten und nicht verwandten) Sprachpaaren möglich. Außerdem zeigen wir, wie unser selbsüberwachter Lernansatz es ermöglicht Stilübertragung ohne parallele Daten zu erlernen und dabei stylistische Umformulierungen von höchster Qualität auf allen geprüften Aufgaben zu erlangen. Für Sequenz-zu-Label Aufgaben unterstreichen wir den Vorteil, welchen hilfsaufgabenseitige Augmentierung über hauptaufgabenseitige Augmentierung hat. Eine Hilfsaufgabe welche sich als besonders hilfreich für die Qualität der Hauptaufgabe herausstellte ist das Teilraumlernen, welches zu beeindruckenden Leistungssteigerungen für (sprachübergreifende) zero-shot Klassifikation ähnlicher und entfernter Zielaufgaben (auch für ähnliche, entfernt verwandte und nicht verwandte Sprachen) führt