ymoslem/acl-abstracts huggingface dataset:
SLTEV: Comprehensive Evaluation of Spoken Language Translation (2021)

Automatic evaluation of Machine Translation (MT) quality has been investigated over several decades. Spoken Language Translation (SLT), esp. when simultaneous, needs to consider additional criteria and does not have a standard evaluation procedure and a widely used toolkit. To fill the gap, we develop SLTev, an open-source tool for assessing SLT in a comprehensive way. SLTev reports the quality, latency, and stability of an SLT candidate output based on the time-stamped transcript and reference translation into a target language. For quality, we rely on sacreBLEU which provides MT evaluation measures such as chrF or BLEU. For latency, we propose two new scoring techniques. For stability, we extend the previously defined measures with a normalized Flicker in our work. We also propose a new averaging of older measures. A preliminary version of SLTev was used in the IWSLT 2020 shared task. Moreover, a growing collection of test datasets directly accessible by SLTev are provided for system evaluation comparable across papers.
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing (2021)

We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained pipelines for 56 languages. Built on a state-of-the-art pretrained language model, Trankit significantly outperforms prior multilingual NLP pipelines over sentence segmentation, part-of-speech tagging, morphological feature tagging, and dependency parsing while maintaining competitive performance for tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. Despite the use of a large pretrained transformer, our toolkit is still efficient in memory usage and speed. This is achieved by our novel plug-and-play mechanism with Adapters where a multilingual pretrained transformer is shared across pipelines for different languages. Our toolkit along with pretrained models and code are publicly available at: \url{https://github.com/nlp-uoregon/trankit}. A demo website for our toolkit is also available at: \url{http://nlp.uoregon.edu/trankit}. Finally, we create a demo video for Trankit at: \url{https://youtu.be/q0KGP3zGjGc}.
DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces (2021)

Recent research efforts in NLP have demonstrated that distributional word vector spaces often encode stereotypical human biases, such as racism and sexism. With word representations ubiquitously used in NLP models and pipelines, this raises ethical issues and jeopardizes the fairness of language technologies. While there exists a large body of work on bias measures and debiasing methods, to date, there is no platform that would unify these research efforts and make bias measuring and debiasing of representation spaces widely accessible. In this work, we present DebIE, the first integrated platform for (1) measuring and (2) mitigating bias in word embeddings. Given an (i) embedding space (users can choose between the predefined spaces or upload their own) and (ii) a bias specification (users can choose between existing bias specifications or create their own), DebIE can (1) compute several measures of implicit and explicit bias and modify the embedding space by executing two (mutually composable) debiasing models. DebIE`s functionality can be accessed through four different interfaces: (a) a web application, (b) a desktop application, (c) a REST-ful API, and (d) as a command-line application. DebIE is available at: debie.informatik.uni-mannheim.de.
A Dashboard for Mitigating the COVID-19 Misinfodemic (2021)

This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter. Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places. Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions. The paper explains how to use BERT models to match tweets with the facts and misinformation and to detect their stance towards such information. The paper also discusses the results of preliminary experiments on analyzing the spatio-temporal spread of misinformation.
EasyTurk: A User-Friendly Interface for High-Quality Linguistic Annotation with Amazon Mechanical Turk (2021)

Amazon Mechanical Turk (AMT) has recently become one of the most popular crowd-sourcing platforms, allowing researchers from all over the world to create linguistic datasets quickly and at a relatively low cost. Amazon provides both a web interface and an API for AMT, but they are not very user-friendly and miss some features that can be useful for NLP researchers. In this paper, we present EasyTurk, a free tool that improves the potential of Amazon Mechanical Turk by adding to it some new features. The tool is free and released under an open source license.
ASAD: Arabic Social media Analytics and unDerstanding (2021)

This system demonstration paper describes ASAD: Arabic Social media Analysis and unDerstanding, a suite of seven individual modules that allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. The suite is made available through a web API and a web interface where users can enter text or upload files.
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet (2021)

In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCOEX takes into account the challenging characteristics of ConceptNet, namely that {--} unlike conventional knowledge graphs {--} nodes are represented as non-canonicalized, free-form text. This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled. A commonly used shortcut to circumvent these problems is to apply string matching. We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph.
A description and demonstration of SAFAR framework (2021)

Several tools and resources have been developed to deal with Arabic NLP. However, a homogenous and flexible Arabic environment that gathers these components is rarely available. In this perspective, we introduce SAFAR which is a monolingual framework developed in accordance with software engineering requirements and dedicated to Arabic language, especially, the modern standard Arabic and Moroccan dialect. After one decade of integration and development, SAFAR possesses today more than 50 tools and resources that can be exploited either using its API or using its web interface.
InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021)

With the increasingly widespread use of Transformer-based models for NLU/NLP tasks, there is growing interest in understanding the inner workings of these models, why they are so effective at a wide range of tasks, and how they can be further tuned and improved. To contribute towards this goal of enhanced explainability and comprehension, we present InterpreT, an interactive visualization tool for interpreting Transformer-based models. In addition to providing various mechanisms for investigating general model behaviours, novel contributions made in InterpreT include the ability to track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeddings through illustrative plots, and identify task-related functions of attention heads by using new metrics. InterpreT is a task agnostic tool, and its functionalities are demonstrated through the analysis of model behaviours for two disparate tasks: Aspect Based Sentiment Analysis (ABSA) and the Winograd Schema Challenge (WSC).
Representing ELMo embeddings as two-dimensional text online (2021)

We describe a new addition to the WebVectors toolkit which is used to serve word embedding models over the Web. The new ELMoViz module adds support for contextualized embedding architectures, in particular for ELMo models. The provided visualizations follow the metaphor of {\textquoteleft}two-dimensional text' by showing lexical substitutes: words which are most semantically similar in context to the words of the input sentence. The system allows the user to change the ELMo layers from which token embeddings are inferred. It also conveys corpus information about the query words and their lexical substitutes (namely their frequency tiers and parts of speech). The module is well integrated into the rest of the WebVectors toolkit, providing lexical hyperlinks to word representations in static embedding models. Two web services have already implemented the new functionality with pre-trained ELMo models for Russian, Norwegian and English.
LOME: Large Ontology Multilingual Extraction (2021)

We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.
MadDog: A Web-based System for Acronym Identification and Disambiguation (2021)

Acronyms and abbreviations are the short-form of longer phrases and they are ubiquitously employed in various types of writing. Despite their usefulness to save space in writing and reader`s time in reading, they also provide challenges for understanding the text especially if the acronym is not defined in the text or if it is used far from its definition in long texts. To alleviate this issue, there are considerable efforts both from the research community and software developers to build systems for identifying acronyms and finding their correct meanings in the text. However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available. Thus, we provide the first web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. The web-based system is publicly available at \url{http://iq.cs.uoregon.edu:5000} and a demo video is available at \url{https://youtu.be/IkSh7LqI42M}. The system source code is also available at \url{https://github.com/amirveyseh/MadDog}.
Graph Matching and Graph Rewriting: GREW tools for corpus exploration, maintenance and conversion (2021)

This article presents a set of tools built around the Graph Rewriting computational framework which can be used to compute complex rule-based transformations on linguistic structures. Application of the graph matching mechanism for corpus exploration, error mining or quantitative typology are also given.
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP (2021)

Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings. The benefits of MaChAmp are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.
SCoT: Sense Clustering over Time: a tool for the analysis of lexical change (2021)

We present Sense Clustering over Time (SCoT), a novel network-based tool for analysing lexical change. SCoT represents the meanings of a word as clusters of similar words. It visualises their formation, change, and demise. There are two main approaches to the exploration of dynamic networks: the discrete one compares a series of clustered graphs from separate points in time. The continuous one analyses the changes of one dynamic network over a time-span. SCoT offers a new hybrid solution. First, it aggregates time-stamped documents into intervals and calculates one sense graph per discrete interval. Then, it merges the static graphs to a new type of dynamic semantic neighbourhood graph over time. The resulting sense clusters offer uniquely detailed insights into lexical change over continuous intervals with model transparency and provenance. SCoT has been successfully used in a European study on the changing meaning of {\textquoteleft}crisis'.
GCM: A Toolkit for Generating Synthetic Code-mixed Text (2021)

Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data. We describe a tool that can automatically generate code-mixed data given parallel data in two languages. We implement two linguistic theories of code-mixing, the Equivalence Constraint theory and the Matrix Language theory to generate all possible code-mixed sentences in the language-pair, followed by sampling of the generated data to generate natural code-mixed sentences. The toolkit provides three modes: a batch mode, an interactive library mode and a web-interface to address the needs of researchers, linguists and language experts. The toolkit can be used to generate unlabeled text data for pre-trained models, as well as visualize linguistic theories of code-mixing. We plan to release the toolkit as open source and extend it by adding more implementations of linguistic theories, visualization techniques and better sampling techniques. We expect that the release of this toolkit will help facilitate more research in code-mixing in diverse language pairs.
T2NER: Transformers based Transfer Learning Framework for Named Entity Recognition (2021)

Recent advances in deep transformer models have achieved state-of-the-art in several natural language processing (NLP) tasks, whereas named entity recognition (NER) has traditionally benefited from long-short term memory (LSTM) networks. In this work, we present a Transformers based Transfer Learning framework for Named Entity Recognition (T2NER) created in PyTorch for the task of NER with deep transformer models. The framework is built upon the Transformers library as the core modeling engine and supports several transfer learning scenarios from sequential transfer to domain adaptation, multi-task learning, and semi-supervised learning. It aims to bridge the gap between the algorithmic advances in these areas by combining them with the state-of-the-art in transformer models to provide a unified platform that is readily extensible and can be used for both the transfer learning research in NER, and for real-world applications. The framework is available at: \url{https://github.com/suamin/t2ner}.
European Language Grid: A Joint Platform for the European Language Technology Community (2021)

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.
A New Surprise Measure for Extracting Interesting Relationships between Persons (2021)

One way to enhance user engagement in search engines is to suggest interesting facts to the user. Although relationships between persons are important as a target for text mining, there are few effective approaches for extracting the interesting relationships between persons. We therefore propose a method for extracting interesting relationships between persons from natural language texts by focusing on their surprisingness. Our method first extracts all personal relationships from dependency trees for the texts and then calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. The unique point of our method is that it does not require any labeled dataset with annotation for the surprising personal relationships. The results of the human evaluation show that the proposed method could extract more interesting relationships between persons from Japanese Wikipedia articles than a popularity-based baseline method. We demonstrate our proposed method as a chrome plugin on google search.
Paladin: an annotation tool based on active and proactive learning (2021)

In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets. By integrating active learning and proactive learning to the annotation task, Paladin makes the task less time-consuming and requiring less human effort. Although Paladin is designed for multi-label settings, the system is flexible and can be adapted to other tasks in single-label settings.
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool (2021)

Few shot learning with large language models has the potential to give individuals without formal machine learning training the access to a wide range of text to text models. We consider how this applies to creative writers and present Story Centaur, a user interface for prototyping few shot models and a set of recombinable web components that deploy them. Story Centaur`s goal is to expose creative writers to few shot learning with a simple but powerful interface that lets them compose their own co-creation tools that further their own unique artistic directions. We build out several examples of such tools, and in the process probe the boundaries and issues surrounding generation with large language models.
FrameForm: An Open-source Annotation Interface for FrameNet (2021)

In this paper, we introduce FrameForm, an open-source annotation tool designed to accommodate predicate annotations based on Frame Semantics. FrameForm is a user-friendly tool for creating, annotating and maintaining computational lexicography projects like FrameNet and has been used while building the Turkish FrameNet. Responsive and open-source, FrameForm can be easily modified to answer the annotation needs of a wide range of different languages.
OCTIS: Comparing and Optimizing Topic models is Simple! (2021)

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration. OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model. The code is available at the following link: \url{https://github.com/MIND-Lab/OCTIS}.
ELITR Multilingual Live Subtitling: Demo and Strategy (2021)

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations. We describe the overall architecture and key processing components. More importantly, we explain our strategy for building a complex system for end-users from numerous individual components, each of which has been tested only in laboratory conditions. The system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages.
Breaking Writer`s Block: Low-cost Fine-tuning of Natural Language Generation Models (2021)

It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models. This is not the case for generation task, which relies on a variety of techniques for controlled language generation. In this paper, we describe a system that fine-tunes a natural language generation model for the problem of solving writer`s block. The fine-tuning changes the conditioning to also include the right context in addition to the left context, as well as an optional list of entities, the size, the genre and a summary of the paragraph that the human author wishes to generate. Our proposed fine-tuning obtains excellent results, even with a small number of epochs and a total cost of USD 150. The system can be accessed as a web-service and all the code is released. A video showcasing the interface and the model is also available.
OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning (2021)

OPUS-CAT is a collection of software which enables translators to use neural machine translation in computer-assisted translation tools without exposing themselves to security and confidentiality risks inherent in online machine translation. OPUS-CAT uses the public OPUS-MT machine translation models, which are available for over a thousand language pairs. The generic OPUS-MT models can be fine-tuned with OPUS-CAT on the desktop using data for a specific client or domain.
Domain Expert Platform for Goal-Oriented Dialog Collection (2021)

Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions, that are necessary for training a particular neural network architecture. In this demonstration paper, we present an easy to use interface and its back-end which is oriented to domain experts for the collection of goal-oriented dialogue samples. The platform not only allows to collect or write sample dialogues in a structured way, but also provides a means for simple annotation and interpretation of the dialogues. The platform itself is language-independent; it depends only on the availability of particular language processing components for a specific language. It is currently being used to collect dialogue samples in Latvian (a highly inflected language) which represent typical communication between students and the student service.