pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice?
Authors: Filip Miletic, Anne Przewozny-Desriaux, Ludovic Tanguy
Date: 2021-11-01 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Change Detection

This study investigates the applicability of semantic change detection methods in descriptively oriented linguistic research. It specifically focuses on contact-induced semantic shifts in Quebec English. We contrast synchronic data from different regions in order to identify the meanings that are specific to Quebec and potentially related to language contact. Type-level embeddings are used to detect new semantic shifts, and token-level embeddings to isolate regionally specific occurrences. We introduce a new 80-item test set and conduct both quantitative and qualitative evaluations. We demonstrate that diachronic word embedding methods can be applied to contact-induced semantic shifts observed in synchrony, obtaining results comparable to the state of the art on similar tasks in diachrony. However, we show that encouraging evaluation results do not translate to practical value in detecting new semantic shifts. Finally, our application of token-level embeddings accelerates manual data exploration and provides an efficient way of scaling up sociolinguistic analyses.
MiSS: An Assistant for Multi-Style Simultaneous Translation
Authors: Zuchao Li, Kevin Parnow, Masao Utiyama, Eiichiro Sumita, Hai Zhao
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Grammatical Error Correction, Machine Translation, Translation

In this paper, we present MiSS, an assistant for multi-style simultaneous translation. Our proposed translation system has five key features: highly accurate translation, simultaneous translation, translation for multiple text styles, back-translation for translation quality evaluation, and grammatical error correction. With this system, we aim to provide a complete translation experience for machine translation users. Our design goals are high translation accuracy, real-time translation, flexibility, and measurable translation quality. Compared with the free commercial translation systems commonly used, our translation assistance system regards the machine translation application as a more complete and fully-featured tool for users. By incorporating additional features and giving the user better control over their experience, we improve translation efficiency and performance. Additionally, our assistant system combines machine translation, grammatical error correction, and interactive edits, and uses a crowdsourcing mode to collect more data for further training to improve both the machine translation and grammatical error correction models. A short video demonstrating our system is available at https://www.youtube.com/watch?v=ZGCo7KtRKd8.
TransIns: Document Translation with Markup Reinsertion
Authors: Jörg Steffen, Josef van Genabith
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Document Translation, NMT, Translation

For many use cases, it is required that MT does not just translate raw text, but complex formatted documents (e.g. websites, slides, spreadsheets) and the result of the translation should reflect the formatting. This is challenging, as markup can be nested, apply to spans contiguous in source but non-contiguous in target etc. Here we present TransIns, a system for non-plain text document translation that builds on the Okapi framework and MT models trained with Marian NMT. We develop, implement and evaluate different strategies for reinserting markup into translated sentences using token alignments between source and target sentences. We propose a simple and effective strategy that compiles down all markup to single source tokens and transfers them to aligned target tokens. A first evaluation shows that this strategy yields highly accurate markup in the translated documents that outperforms the markup quality found in documents translated with popular translation services. We release TransIns under the MIT License as open-source software on https://github.com/DFKI-MLT/TransIns. An online demonstrator is available at https://transins.dfki.de.
ET: A Workstation for Querying, Editing and Evaluating Annotated Corpora
Authors: Elvis de Souza, Cláudia Freitas
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

In this paper we explore the functionalities of ET, a suite designed to support linguistic research and natural language processing tasks using corpora annotated in the CoNLL-U format. These goals are achieved by two integrated environments – Interrogatório, an environment for querying and editing annotated corpora, and Julgamento, an environment for assessing their quality. ET is open-source, built on different Python Web technologies and has Web demonstrations available on-line. ET has been intensively used in our research group for over two years, being the chosen framework for several linguistic and NLP-related studies conducted by its researchers.
KOAS: Korean Text Offensiveness Analysis System
Authors: San-Hee Park, Kang-Min Kim, Seonhee Cho, Jun-Hyung Park, Hyuntae Park, Hyuna Kim, Seongwon Chung, SangKeun Lee
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Abusive Language, Multi-Task Learning

Warning: This manuscript contains a certain level of offensive expression. As communication through social media platforms has grown immensely, the increasing prevalence of offensive language online has become a critical problem. Notably in Korea, one of the countries with the highest Internet usage, automatic detection of offensive expressions has recently been brought to attention. However, morphological richness and complex syntax of Korean causes difficulties in neural model training. Furthermore, most of previous studies mainly focus on the detection of abusive language, disregarding implicit offensiveness and underestimating a different degree of intensity. To tackle these problems, we present KOAS, a system that fully exploits both contextual and linguistic features and estimates an offensiveness score for a text. We carefully designed KOAS with a multi-task learning framework and constructed a Korean dataset for offensive analysis from various domains. Refer for a detailed demonstration.
RepGraph: Visualising and Analysing Meaning Representation Graphs
Authors: Jaron Cohen, Roy Cohen, Edan Toledo, Jan Buys
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

We present RepGraph, an open source visualisation and analysis tool for meaning representation graphs. Graph-based meaning representations provide rich semantic annotations, but visualising them clearly is more challenging than for fully lexicalized representations. Our application provides a seamless, unifying interface with which to visualise, manipulate and analyse semantically parsed graph data represented in a JSON-based serialisation format. RepGraph visualises graphs in multiple formats, with an emphasis on showing the relation between nodes and their corresponding token spans, whilst keeping the representation compact. Additionally, the web-based tool provides NLP researchers with a clear, visually intuitive way of interacting with these graphs, and includes a number of graph analysis features. The tool currently supports the DMRS, EDS, PTG, UCCA, and AMR semantic frameworks. A live demo is available at https://repgraph.vercel.app/.
Semantic Context Path Labeling for Semantic Exploration of User Reviews
Authors: Salah Aït-Mokhtar, Caroline Brun, Yves Hoppenot, Agnes Sandor
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

In this paper we present a prototype demonstrator showcasing a novel method to perform semantic exploration of user reviews. The system enables effective navigation in a rich contextual semantic schema with a large number of structured classes indicating relevant information. In order to identify instances of the structured classes in the reviews, we defined a new Information Extraction task called Semantic Context Path (SCP) labeling, which simultaneously assigns types and semantic roles to entity mentions. Reviews can rapidly be explored based on the fine-grained and structured semantic classes. As a proof-of-concept, we have implemented this system for reviews on Points-of-Interest, in English and Korean.
SPRING Goes Online: End-to-End AMR Parsing and Generation
Authors: Rexhina Blloshmi, Michele Bevilacqua, Edoardo Fabiano, Valentina Caruso, Roberto Navigli
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: AMR Parsing

In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by the general public. It provides, among other things, a highly interactive visualization platform and a feedback mechanism to obtain user suggestions for further improvements of the system’s output. Moreover, our RESTful APIs enable easy integration of SPRING in downstream applications where AMR structures are needed. Finally, we make SPRING Online Services freely available at http://nlp.uniroma1.it/spring and, in addition, we release extra model checkpoints to be used with the original SPRING Python code.
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit
Authors: Changhan Wang, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Ann Lee, Peng-Jen Chen, Jiatao Gu, Juan Pino
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Speech Synthesis, text-to-speech, Text to Speech

This paper presents fairseq Sˆ2, a fairseq extension for speech synthesis. We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. To facilitate faster iteration of development and analysis, a suite of automatic metrics is included. Apart from the features added specifically for this extension, fairseq Sˆ2 also benefits from the scalability offered by fairseq and can be easily integrated with other state-of-the-art systems provided in this framework. The code, documentation, and pre-trained models will be made available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.
Press Freedom Monitor: Detection of Reported Press and Media Freedom Violations in Twitter and News Articles
Authors: Tariq Yousef, Antje Schlaf, Janos Borst, Andreas Niekler, Gerhard Heyer
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Articles

Freedom of the press and media is of vital importance for democratically organised states and open societies. We introduce the Press Freedom Monitor, a tool that aims to detect reported press and media freedom violations in news articles and tweets. It is used by press and media freedom organisations to support their daily monitoring and to trigger rapid response actions. The Press Freedom Monitor enables the monitoring experts to get a fast overview over recently reported incidents and it has shown an impressive performance in this regard. This paper presents our work on the tool, starting with the training phase, which comprises defining the topic-related keywords to be used for querying APIs for news and Twitter content and evaluating different machine learning models based on a training dataset specifically created for our use case. Then, we describe the components of the production pipeline, including data gathering, duplicates removal, country mapping, case mapping and the user interface. We also conducted a usability study to evaluate the effectiveness of the user interface, and describe improvement plans for future work.
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations
Authors: Jin Zhao, Nianwen Xue, Jens Van Gysel, Jinho D. Choi
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

We present UMR-Writer, a web-based application for annotating Uniform Meaning Representations (UMR), a graph-based, cross-linguistically applicable semantic representation developed recently to support the development of interpretable natural language applications that require deep semantic analysis of texts. We present the functionalities of UMR-Writer and discuss the challenges in developing such a tool and how they are addressed.
LexiClean: An annotation tool for rapid multi-task lexical normalisation
Authors: Tyler Bikaun, Tim French, Melinda Hodkiewicz, Michael Stewart, Wei Liu
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

NLP systems are often challenged by difficulties arising from noisy, non-standard, and domain specific corpora. The task of lexical normalisation aims to standardise such corpora, but currently lacks suitable tools to acquire high-quality annotated data to support deep learning based approaches. In this paper, we present LexiClean, the first open-source web-based annotation tool for multi-task lexical normalisation. LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide. We demonstrate the usefulness of our tool through a case study on two sets of noisy corpora derived from the specialised-domain of industrial mining. We show that LexiClean allows for the rapid and efficient development of high-quality parallel corpora. A demo of our system is available at: https://youtu.be/P7_ooKrQPDU.
T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP
Authors: Raymond Li, Wen Xiao, Lanjun Wang, Hyeju Jang, Giuseppe Carenini
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model’s intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence. Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements. Our framework is available at: https://github.com/raymondzmc/T3-Vis.
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data
Authors: Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Language Modelling

When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at http://www.openframing.org, via an API http://www.openframing.org:5000/docs/, or through our GitHub page https://github.com/vibss2397/openFraming.
IrEne-viz: Visualizing Energy Consumption of Transformer Models
Authors: Yash Kumar Lal, Reetu Singh, Harsh Trivedi, Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models. We present the IrEne-viz tool, an online platform for visualizing and exploring energy consumption of various Transformer-based models easily. Additionally, we release a public API that can be used to access granular information about energy consumption of transformer models and their components. The live demo is available at http://stonybrooknlp.github.io/irene/demo/.
CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset
Authors: Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, Yafei Shi
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Chinese Named Entity Recognition, Management, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96% and +2.57% F1 respectively in model performance.
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation
Authors: Riccardo Orlando, Simone Conia, Fabrizio Brignone, Francesco Cecconi, Roberto Navigli
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: All, Word Sense Disambiguation

Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models. Unfortunately, such systems are still not available as ready-to-use end-to-end packages, making it difficult for researchers to take advantage of their performance. The only alternative for a user interested in applying WSD to downstream tasks is to rely on currently available end-to-end WSD systems, which, however, still rely on graph-based heuristics or non-neural machine learning algorithms. In this paper, we fill this gap and propose AMuSE-WSD, the first end-to-end system to offer high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. We hope that AMuSE-WSD will provide a stepping stone for the integration of meaning into real-world applications and encourage further studies in lexical semantics. AMuSE-WSD is available online at http://nlp.uniroma1.it/amuse-wsd.
SeqAttack: On Adversarial Attacks for Named Entity Recognition
Authors: Walter Simoncini, Gerasimos Spanakis
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), text-classification, Text Classification, token-classification, Token Classification

Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines. Adversarial attacks have been shown to greatly affect the performance of text classification systems but knowledge about their effectiveness against named entity recognition models is limited. This paper investigates the effectiveness and portability of adversarial attacks from text classification to named entity recognition and the ability of adversarial training to counteract these attacks. We find that character-level and word-level attacks are the most effective, but adversarial training can grant significant protection at little to no expense of standard performance. Alongside our results, we also release SeqAttack, a framework to conduct adversarial attacks against token classification models (used in this work for named entity recognition) and a companion web application to inspect and cherry pick adversarial examples.
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles
Authors: Simone Conia, Riccardo Orlando, Fabrizio Brignone, Francesco Cecconi, Roberto Navigli
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Semantic Role Labeling, Sentence

Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling. In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of-the-art system capable of annotating text with predicate sense and semantic role labels from 7 predicate-argument structure inventories in more than 40 languages. We hope that our system – with its easy-to-use RESTful API and Web interface – will become a valuable tool for the research community, encouraging the integration of sentence-level semantics into cross-lingual downstream tasks. InVeRo-XL is available online at http://nlp.uniroma1.it/invero.
Chandler: An Explainable Sarcastic Response Generator
Authors: Silviu Oprea, Steven Wilson, Walid Magdy
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

We introduce Chandler, a system that generates sarcastic responses to a given utterance. Previous sarcasm generators assume the intended meaning that sarcasm conceals is the opposite of the literal meaning. We argue that this traditional theory of sarcasm provides a grounding that is neither necessary, nor sufficient, for sarcasm to occur. Instead, we ground our generation process on a formal theory that specifies conditions that unambiguously differentiate sarcasm from non-sarcasm. Furthermore, Chandler not only generates sarcastic responses, but also explanations for why each response is sarcastic. This provides accountability, crucial for avoiding miscommunication between humans and conversational agents, particularly considering that sarcastic communication can be offensive. In human evaluation, Chandler achieves comparable or higher sarcasm scores, compared to state-of-the-art generators, while generating more diverse responses, that are more specific and more coherent to the input.