United Arab Emirates

We present PromptLS, a method for fine-tuning large pre-trained Language Models (LM) to perform the task of Lexical Simplification. We use a predefined template to attain appropriate replacements for a term, and fine-tune a LM using this template on language specific datasets. We filter candidate lists in post-processing to improve accuracy. We demonstrate that our model can work in a) a zero shot setting (where we only require a pre-trained LM), b) a fine-tuned setting (where language-specific data is required), and c) a multilingual setting (where the model is pre-trained across multiple languages and fine-tuned in an specific language). Experimental results show that, although the zero-shot setting is competitive, its performance is still far from the fine-tuned setting. Also, the multilingual is unsurprisingly worse than the fine-tuned model. Among all TSAR-2022 Shared Task participants, our team was ranked second in Spanish and third in English.

EMNLP 2022

UoM&amp;MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification

prompt-learning

lexical simplification

multilingual

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification

workshop paper

In this paper, we describe the system we presented at the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022) regarding the shared task on Lexical Simplification for English, Portuguese, and Spanish. We proposed an unsupervised approach in two steps: First, we used a masked language model with word masking for each language to extract possible candidates for the replacement of a difficult word; second, we ranked the candidates according to three different Transformer-based metrics. Finally, we determined our list of candidates based on the lowest average rank across different metrics.

PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps

Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier-to-read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team "teamPN" for the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi-level and modular pipeline where the target text is treated according to its semantics (Part of Speech Tag). The pipeline is multi-level as we utilize multiple source models to find potential candidates for replacement. It is modular as we can switch the source models and their weighting in the final re-ranking.

teamPN at TSAR-2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach

In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system introduced in Qiang et al. (2020) in the following ways: For the subtask of simplification candidate selection, it utilizes a RoBERTa transformer language model and expands the size of the generated candidate list. For subsequent substitution ranking, it introduces a new feature weighting scheme and adopts a candidate filtering method based on textual entailment to maximize semantic similarity between the target word and its simplification. Our best-performing system improves LSBert by 5.9\% accuracy and achieves second place out of 33 ranked solutions.

MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders

Previous state-of-the-art models for lexical simplification consist of complex pipelines with several components, each of which requires deep technical knowledge and fine-tuned interaction to achieve its full potential. As an alternative, we describe a frustratingly simple pipeline based on prompted GPT-3 responses, beating competing approaches by a wide margin in settings with few training instances. Our best-performing submission to the English language track of the TSAR-2022 shared task consists of an "ensemble" of six different prompt templates with varying context levels. As a late-breaking result, we further detail a language transfer technique that allows simplification in languages other than English. Applied to the Spanish and Portuguese subset, we achieve state-of-the-art results with only minor modification to the original prompts. Aside from detailing the implementation and setup, we spend the remainder of this work discussing the particularities of prompting and implications for future work. Code for the experiments is available online at https://github.com/dennlinger/TSAR-2022-Shared-Task.

UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?

This paper describes the lexical simplification system RCML submitted to the English language track of the TSAR-2022 Shared Task. The system leverages a pre-trained language model to generate contextually plausible substitution candidates which are then ranked according to their simplicity as well as their grammatical and semantic similarity to the target complex word. Our submissions secure 6th and 7th places out of 33, improving over the SOTA baseline for 27 out of the 51 metrics.

RCML at TSAR-2022 Shared Task: Lexical Simplification With Modular Substitution Candidate Ranking

This paper describes team GMU-WLV submission to the TSAR shared-task on multilingual lexical simplification. The goal of the task is to automatically provide a set of candidate substitutions for complex words in context. The organizers provided participants with ALEXSIS a manually annotated dataset with instances split between a small trial set with a dozen instances in each of the three languages of the competition (English, Portuguese, Spanish) and a test set with over 300 instances in the three aforementioned languages. To cope with the lack of training data, participants had to either use alternative data sources or pre-trained language models. We experimented with monolingual models: BERTimbau, ELECTRA, and RoBERTA-largeBNE. Our best system achieved 1st place out of sixteen systems for Portuguese, 8th out of thirty-three systems for English, and 6th out of twelve systems for Spanish.

GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models

Most existing proposals about anaphoric zero pronoun (AZP) resolution regard full mention coreference and AZP resolution as two independent tasks, even though the two tasks are clearly related. The main issues that need tackling to develop a joint model for zero and non-zero mentions are the difference between the two types of arguments (zero pronouns, being null, provide no nominal information) and the lack of annotated datasets of a suitable size in which both types of arguments are annotated for languages other than Chinese and Japanese. In this paper, we introduce two architectures for jointly resolving AZPs and non-AZPs, and evaluate them on Arabic, a language for which, as far as we know, there has been no prior work on joint resolution. Doing this also required creating a new version of the Arabic subset of the standard coreference resolution dataset used for the CoNLL-2012 shared task (Pradhan et al.,2012) in which both zeros and non-zeros are included in a single dataset.

Joint Coreference Resolution for Zeros and non-Zeros in Arabic

Contrastive learning (CL) has brought significant progress to various NLP tasks. Despite such a progress, CL has not been applied to Arabic NLP. Nor is it clear how much benefits it could bring to particular classes of tasks such as social meaning (e.g., sentiment analysis, dialect identification, hate speech detection). In this work, we present a comprehensive benchmark study of state-of-the-art supervised CL methods on a wide array of Arabic social meaning tasks. Through an extensive empirical analysis, we show that CL methods outperform vanilla finetuning on most of the tasks. We also show that CL can be data efficient and quantify this efficiency, demonstrating the promise of these methods in low-resource settings vis-a-vis the particular downstream tasks (especially label granularity).

A Benchmark Study of Contrastive Learning for Arabic Social Meaning

In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop. The task of gender rewriting refers to generating alternatives of a given sentence to match different target user gender contexts (e.g., a female speaker with a male listener, a male speaker with a male listener, etc.). This requires changing the grammatical gender (masculine or feminine) of certain words referring to the users. In this task, we focus on Arabic, a gender-marking morphologically rich language. A total of five teams from four countries participated in the shared task.

The Shared Task on Gender Rewriting

Propaganda is defined as an expression of opinion or action by individuals or groups deliberately designed to influence opinions or actions of other individuals or groups with reference to predetermined ends and this is achieved by means of well-defined rhetorical and psychological devices. Currently, propaganda (or persuasion) techniques have been commonly used on social media to manipulate or mislead social media users. Automatic detection of propaganda techniques from textual, visual, or multimodal content has been studied recently, however, major of such efforts are focused on English language content. In this paper, we propose a shared task on detecting propaganda techniques for Arabic textual content. We have done a pilot annotation of 200 Arabic tweets, which we plan to extend to 2,000 tweets, covering diverse topics.  We hope that the shared task will help in building a community for Arabic propaganda detection. The dataset will be made publicly available, which can help in future studies.

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PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps

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Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification