SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German

Swiss German is a dialect continuum whose natively acquired dialects significantly differ from the formal variety of the language. These dialects are mostly used for verbal communication and do not have standard orthography. This has led to a lack of annotated datasets, rendering the use of many NLP methods infeasible. In this paper, we introduce the first annotated parallel corpus of spoken Swiss German across 8 major dialects, plus a Standard German reference. Our goal has been to create and to make available a basic dataset for employing data-driven NLP applications in Swiss German. We present our data collection procedure in detail and validate the quality of our corpus by conducting experiments with the recent neural models for speech synthesis

ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting

Bilingual Lexicon Induction (BLI), where words are translated between two languages, is an important NLP task. While noticeable progress on BLI in rich resource languages using static word embeddings has been achieved. The word translation performance can be further improved by incorporating information from contextualized word embeddings. In this paper, we introduce ProMap, a novel approach for BLI that leverages the power of prompting pretrained multilingual and multidialectal language models to address these challenges. To overcome the employment of subword tokens in these models, ProMap relies on an effective padded prompting of language models with a seed dictionary that achieves good performance when used independently. We also demonstrate the effectiveness of ProMap in re-ranking results from other BLI methods such as with aligned static word embeddings. When evaluated on both rich-resource and low-resource languages, ProMap consistently achieves state-of-the-art results. Furthermore, ProMap enables strong performance in few-shot scenarios (even with less than 10 training examples), making it a valuable tool for low-resource language translation. Overall, we believe our method offers both exciting and promising direction for BLI in general and low-resource languages in particular. ProMap code and data are available at \url{https://github.com/4mekki4/promap}.Comment: To appear in IJCNLP-AACL 202

The Paradigm Discovery Problem

This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evaluation metrics for judging systems. Using currently available resources, we construct datasets for the task. We also devise a heuristic benchmark for the PDP and report empirical results on five diverse languages. Our benchmark system first makes use of word embeddings and string similarity to cluster forms by cell and by paradigm. Then, we bootstrap a neural transducer on top of the clustered data to predict words to realize the empty paradigm slots. An error analysis of our system suggests clustering by cell across different inflection classes is the most pressing challenge for future work. Our code and data are available for public use.Comment: Forthcoming at ACL 202

Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging

Semitic languages can be highly ambiguous, having several interpretations of the same surface forms, and morphologically rich, having many morphemes that realize several morphological features. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), where we also model surface form normalization, or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern Standard Arabic, with 20% relative error reduction, and Egyptian Arabic (a dialectal variant of Arabic), with 11% reduction

A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

Sociolinguistically Driven Approaches for Just Natural Language Processing

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work