pwc-archive/papers-with-abstracts huggingface dataset:
Towards Reference-Aware FrameNet Annotation
Authors: Levi Remijnse, Gosse Minnema
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events. We show that frames made relevant by the knowledge of the real-world event can be captured by complementing standard lexicon-driven FrameNet annotations with frame annotations derived through pragmatic inference. We propose a two-layered annotation scheme with a {`}strict{'} FrameNet-compatible lexical layer and a {`}loose{'} layer capturing frames that are inferred from referential data.
Frame-Based Annotation of Multimodal Corpora: Tracking (A)Synchronies in Meaning Construction
Authors: Frederico Belcavello, Marcelo Viridiano, Alex Diniz da Costa, re, Ely Edison da Silva Matos, Tiago Timponi Torrent
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Text Generation, Translation

Multimodal aspects of human communication are key in several applications of Natural Language Processing, such as Machine Translation and Natural Language Generation. Despite recent advances in integrating multimodality into Computational Linguistics, the merge between NLP and Computer Vision techniques is still timid, especially when it comes to providing fine-grained accounts for meaning construction. This paper reports on research aiming to determine appropriate methodology and develop a computational tool to annotate multimodal corpora according to a principled structured semantic representation of events, relations and entities: FrameNet. Taking a Brazilian television travel show as corpus, a pilot study was conducted to annotate the frames that are evoked by the audio and the ones that are evoked by visual elements. We also implemented a Multimodal Annotation tool which allows annotators to choose frames and locate frame elements both in the text and in the images, while keeping track of the time span in which those elements are active in each modality. Results suggest that adding a multimodal domain to the linguistic layer of annotation and analysis contributes both to enrich the kind of information that can be tagged in a corpus, and to enhance FrameNet as a model of linguistic cognition.
Combining Conceptual and Referential Annotation to Study Variation in Framing
Authors: Marten Postma, Levi Remijnse, Filip Ilievski, Antske Fokkens, Sam Titarsolej, Piek Vossen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We introduce an annotation tool whose purpose is to gain insights into variation of framing by combining FrameNet annotation with referential annotation. English FrameNet enables researchers to study variation in framing at the conceptual level as well through its packaging in language. We enrich FrameNet annotations in two ways. First, we introduce the referential aspect. Secondly, we annotate on complete texts to encode connections between mentions. As a result, we can analyze the variation of framing for one particular event across multiple mentions and (cross-lingual) documents. We can examine how an event is framed over time and how core frame elements are expressed throughout a complete text. The data model starts with a representation of an event type. Each event type has many incidents linked to it, and each incident has several reference texts describing it as well as structured data about the incident. The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.
FrameNet Annotations Alignment using Attention-based Machine Translation
Authors: Gabriel Marzinotto
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Decoder, Machine Translation, NMT, Translation, Word Alignment

This paper presents an approach to project FrameNet annotations into other languages using attention-based neural machine translation (NMT) models. The idea is to use an NMT encoder-decoder attention matrix to propose a word-to-word correspondence between the source and the target language. We combine this word alignment along with a set of simple rules to securely project the FrameNet annotations into the target language. We successfully implemented, evaluated and analyzed this technique on the English-to-French configuration. First, we analyze the obtained FrameNet lexicon qualitatively. Then, we use existing French FrameNet corpora to assert the quality of the translation. Finally, we trained a BERT-based FrameNet parser using the projected annotations and compared it to a BERT baseline. Results show substantial improvements in the French language, giving evidence to support that our approach could help to propagate FrameNet data-set on other languages.
Greek within the Global FrameNet Initiative: Challenges and Conclusions so far
Authors: Voula Giouli, Vera Pilitsidou, Hephaestion Christopoulos
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

Large coverage lexical resources that bear deep linguistic information have always been considered useful for many natural language processing (NLP) applications including Machine Translation (MT). In this respect, Frame-based resources have been developed for many languages following Frame Semantics and the Berkeley FrameNet project. However, to a great extent, all those efforts have been kept fragmented. Consequentially, the Global FrameNet initiative has been conceived of as a joint effort to bring together FrameNets in different languages. The proposed paper is aimed at describing ongoing work towards developing the Greek (EL) counterpart of the Global FrameNet and our efforts to contribute to the Shared Annotation Task. In the paper, we will elaborate on the annotation methodology employed, the current status and progress made so far, as well as the problems raised during annotation.
Using Verb Frames for Text Difficulty Assessment
Authors: John Lee, Meichun Liu, Tianyuan Cai
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.
Deriving a PropBank Corpus from Parallel FrameNet and UD Corpora
Authors: Normunds Gruzitis, Roberts Dar{\c{g}}is, Laura Rituma, Gunta Ne{\v{s}}pore-B{\=e}rzkalne, Baiba Saulite
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We propose an approach for generating an accurate and consistent PropBank-annotated corpus, given a FrameNet-annotated corpus which has an underlying dependency annotation layer, namely, a parallel Universal Dependencies (UD) treebank. The PropBank annotation layer of such a multi-layer corpus can be semi-automatically derived from the existing FrameNet and UD annotation layers, by providing a mapping configuration from lexical units in [a non-English language] FrameNet to [English language] PropBank predicates, and a mapping configuration from FrameNet frame elements to PropBank semantic arguments for the given pair of a FrameNet frame and a PropBank predicate. The latter mapping generally depends on the underlying UD syntactic relations. To demonstrate our approach, we use Latvian FrameNet, annotated on top of Latvian UD Treebank, for generating Latvian PropBank in compliance with the Universal Propositions approach.
Building the Emirati Arabic FrameNet
Authors: Andrew Gargett, Tommi Leung
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The Emirati Arabic FrameNet (EAFN) project aims to initiate a FrameNet for Emirati Arabic, utilizing the Emirati Arabic Corpus. The goal is to create a resource comparable to the initial stages of the Berkeley FrameNet. The project is divided into manual and automatic tracks, based on the predominant techniques being used to collect frames in each track. Work on the EAFN is progressing, and we here report on initial results for annotations and evaluation. The EAFN project aims to provide a general semantic resource for the Arabic language, sure to be of interest to researchers from general linguistics to natural language processing. As we report here, the EAFN is well on target for the first release of data in the coming year.
Exploring Crosslinguistic Frame Alignment
Authors: Collin F. Baker, Arthur Lorenzi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The FrameNet (FN) project at the International Computer Science Institute in Berkeley (ICSI), which documents the core vocabulary of contemporary English, was the first lexical resource based on Fillmore{'}s theory of Frame Semantics. Berkeley FrameNet has inspired related projects in roughly a dozen other languages, which have evolved somewhat independently; the current Multilingual FrameNet project (MLFN) is an attempt to find alignments between all of them. The alignment problem is complicated by the fact that these projects have adhered to the Berkeley FrameNet model to varying degrees, and they were also founded at different times, when different versions of the Berkeley FrameNet data were available. We describe several new methods for finding relations of similarity between semantic frames across languages. We will demonstrate ViToXF, a new tool which provides interactive visualizations of these cross-lingual relations, between frames, lexical units, and frame elements, based on resources such as multilingual dictionaries and on shared distributional vector spaces, making clear the strengths and weaknesses of different alignment methods.
Building Multilingual Specialized Resources Based on FrameNet: Application to the Field of the Environment
Authors: Marie-Claude L{'} Homme, Beno{\^\i}t Robichaud, Carlos Subirats
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The methodology developed within the FrameNet project is being used to compile resources in an increasing number of specialized fields of knowledge. The methodology along with the theoretical principles on which it is based, i.e. Frame Semantics, are especially appealing as they allow domain-specific resources to account for the conceptual background of specialized knowledge and to explain the linguistic properties of terms against this background. This paper presents a methodology for building a multilingual resource that accounts for terms of the environment. After listing some lexical and conceptual differences that need to be managed in such a resource, we explain how the FrameNet methodology is adapted for describing terms in different languages. We first applied our methodology to French and then extended it to English. Extensions to Spanish, Portuguese and Chinese were made more recently. Up to now, we have defined 190 frames: 112 frames are new; 38 are used as such; and 40 are slightly different (a different number of obligatory participants; a significant alternation, etc.) when compared to Berkeley FrameNet.
Creating a Sentiment Lexicon with Game-Specific Words for Analyzing NPC Dialogue in The Elder Scrolls V: Skyrim
Authors: Th{\'e}r{\`e}se Bergsma, Judith van Stegeren, Mari{\"e}t Theune
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis

A weak point of rule-based sentiment analysis systems is that the underlying sentiment lexicons are often not adapted to the domain of the text we want to analyze. We created a game-specific sentiment lexicon for video game Skyrim based on the E-ANEW word list and a dataset of Skyrim{'}s in-game documents. We calculated sentiment ratings for NPC dialogue using both our lexicon and E-ANEW and compared the resulting sentiment ratings to those of human raters. Both lexicons perform comparably well on our evaluation dialogues, but the game-specific extension performs slightly better on the dominance dimension for dialogue segments and the arousal dimension for full dialogues. To our knowledge, this is the first time that a sentiment analysis lexicon has been adapted to the video game domain.
ClueMeIn: Obtaining More Specific Image Labels Through a Game
Authors: Christopher Harris
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The ESP Game (also known as the Google Image Labeler) demonstrated how the crowd could perform a task that is straightforward for humans but challenging for computers {--} providing labels for images. The game facilitated the task of basic image labeling; however, the labels generated were non-specific and limited the ability to distinguish similar images from one another, limiting its ability in search tasks, annotating images for the visually impaired, and training computer vision machine algorithms. In this paper, we describe ClueMeIn, an entertaining web-based game with a purpose that generates more detailed image labels than the ESP Game. We conduct experiments to generate specific image labels, show how the results can lead to improvements in the accuracy of image searches over image labels generated by the ESP Game when using the same public dataset.
Cipher: A Prototype Game-with-a-Purpose for Detecting Errors in Text
Authors: Liang Xu, Jon Chamberlain
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Errors commonly exist in machine-generated documents and publication materials; however, some correction algorithms do not perform well for complex errors and it is costly to employ humans to do the task. To solve the problem, a prototype computer game called Cipher was developed that encourages people to identify errors in text. Gamification is achieved by introducing the idea of steganography as the entertaining game element. People play the game for entertainment while they make valuable annotations to locate text errors. The prototype was tested by 35 players in a evaluation experiment, creating 4,764 annotations. After filtering the data, the system detected manually introduced text errors and also genuine errors in the texts that were not noticed when they were introduced into the game.
Game Design Evaluation of GWAPs for Collecting Word Associations
Authors: Mathieu Lafourcade, Le Brun Nathalie
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Game Design

GWAP design might have a tremendous effect on its popularity of course but also on the quality of the data collected. In this paper, a comparison is undertaken between two GWAPs for building term association lists, namely JeuxDeMots and Quicky Goose. After comparing both game designs, the Cohen kappa of associative lists in various configurations is computed in order to assess likeness and differences of the data they provide.
The Challenge of the TV game La Ghigliottina to NLP
Authors: Federico Sangati, Antonio Pascucci, Johanna Monti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we describe a Telegram bot, Mago della Ghigliottina (Ghigliottina Wizard), able to solve La Ghigliottina game (The Guillotine), the final game of the Italian TV quiz show L{'}Eredit{\`a}. Our system relies on linguistic resources and artificial intelligence and achieves better results than human players (and competitors of L{'}Eredit{\`a} too). In addition to solving a game, Mago della Ghigliottina can also generate new game instances and challenge the users to match the solution.
A 3D Role-Playing Game for Abusive Language Annotation
Authors: Federico Bonetti, Sara Tonelli
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abusive Language

Gamification has been applied to many linguistic annotation tasks, as an alternative to crowdsourcing platforms to collect annotated data in an inexpensive way. However, we think that still much has to be explored. Games with a Purpose (GWAPs) tend to lack important elements that we commonly see in commercial games, such as 2D and 3D worlds or a story. Making GWAPs more similar to full-fledged video games in order to involve users more easily and increase dissemination is a demanding yet interesting ground to explore. In this paper we present a 3D role-playing game for abusive language annotation that is currently under development.
Designing a GWAP for Collecting Naturally Produced Dialogues for Low Resourced Languages
Authors: Zulipiye Yusupujiang, Jonathan Ginzburg
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper we present a new method for collecting naturally generated dialogue data for a low resourced language, (specifically here{---}Uyghur). We plan to build a games with a purpose (GWAPs) to encourage native speakers to actively contribute dialogue data to our research project. Since we aim to characterize the response space of queries in Uyghur, we design various scenarios for conversations that yield to questions being posed and responded to. We will implement the GWAP with the RPG Maker MV Game Engine, and will integrate the chatroom system in the game with the Dialogue Experimental Toolkit (DiET). DiET will help us improve the data collection process, and most importantly, make us have some control over the interactions among the participants.
CALLIG: Computer Assisted Language Learning using Improvisation Games
Authors: Lu{\'\i}s Morgado da Costa, Joanna Ut-Seong Sio
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we present the ongoing development of CALLIG {--} a web system that uses improvisation games in Computer Assisted Language Learning (CALL). Improvisation games are structured activities with built-in constraints where improvisers are asked to generate a lot of different ideas and weave a diverse range of elements into a sensible narrative spontaneously. This paper discusses how computer-based language games can be created combining improvisation elements and language technology. In contrast with traditional language exercises, improvisational language games are open and unpredictable. CALLIG encourages spontaneity and witty language use. It also provides opportunities for collecting useful data for many NLP applications.
Bringing Roguelikes to Visually-Impaired Players by Using NLP
Authors: Jes{\'u}s Vilares, Carlos G{\'o}mez-Rodr{\'\i}guez, Lu{\'\i}s Fern{\'a}ndez-N{\'u}{\~n}ez, Dar{\'\i}o Penas, Jorge Viteri
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Text Generation

Although the roguelike video game genre has a large community of fans (both players and developers) and the graphic aspect of these games is usually given little relevance (ASCII-based graphics are not rare even today), their accessibility for blind players and other visually-impaired users remains a pending issue. In this document, we describe an initiative for the development of roguelikes adapted to visually-impaired players by using Natural Language Processing techniques, together with the first completed games resulting from it. These games were developed as Bachelor{'}s and Master{'}s theses. Our approach consists in integrating a multilingual module that, apart from the classic ASCII-based graphical interface, automatically generates text descriptions of what is happening within the game. The visually-impaired user can then read such descriptions by means of a screen reader. In these projects we seek expressivity and variety in the descriptions, so we can offer the users a fun roguelike experience that does not sacrifice any of the key characteristics that define the genre. Moreover, we intend to make these projects easy to extend to other languages, thus avoiding costly and complex solutions. KEYWORDS: Natural Language Generation, roguelikes, visually-impaired users
Demonstration of a Serious Game for Spoken Language Experiments - GDX
Authors: Daniel Duran, Lew, Natalie owski
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Unity

Increasing efforts are put into gamification of experimentation software in psychology and educational applications and the development of serious games. Computer-based experiments with game-like features have been developed previously for research on cognitive skills, cognitive processing speed, working memory, attention, learning, problem solving, group behavior and other phenomena. It has been argued that computer game experiments are superior to traditional computerized experimentation methods in laboratory tasks in that they represent holistic, meaningful, and natural human activity. We present a novel experimental framework for forced choice categorization tasks or speech perception studies in the form of a computer game, based on the Unity Engine {--} the Gamified Discrimination Experiments engine (GDX). The setting is that of a first person shooter game with the narrative background of an alien invasion on earth. We demonstrate the utility of our game as a research tool with an application focusing on attention to fine phonetic detail in natural speech perception. The game-based framework is additionally compared against a traditional experimental setup in an auditory discrimination task. Applications of this novel game-based framework are multifarious within studies on all aspects of spoken language perception.
Aggregation Driven Progression System for GWAPs
Authors: Osman Doruk Kicikoglu, Richard Bartle, Jon Chamberlain, Silviu Paun, Massimo Poesio
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.
Automatic Annotation of Werewolf Game Corpus with Players Revealing Oneselves as Seer/Medium and Divination/Medium Results
Authors: Youchao Lin, Miho Kasamatsu, Tengyang Chen, Takuya Fujita, Huanjin Deng, Takehito Utsuro
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

While playing the communication game {``}Are You a Werewolf{''}, a player always guesses other players{'} roles through discussions, based on his own role and other players{'} crucial utterances. The underlying goal of this paper is to construct an agent that can analyze the participating players{'} utterances and play the werewolf game as if it is a human. For a step of this underlying goal, this paper studies how to accumulate werewolf game log data annotated with identification of players revealing oneselves as seer/medium, the acts of the divination and the medium and declaring the results of the divination and the medium. In this paper, we divide the whole task into four sub tasks and apply CNN/SVM classifiers to each sub task and evaluate their performance.