T abouid: a W ikipedia-based word guessing game (2020)

Abstract: We present Tabouid, a word-guessing game automatically generated from Wikipedia. Tabouid contains 10,000 (virtual) cards in English, and as many in French, covering not only words and linguistic expressions but also a variety of topics including artists, historical events or scientific concepts. Each card corresponds to a Wikipedia article, and conversely, any article could be turned into a card. A range of relatively simple NLP and machine-learning techniques are effectively integrated into a two-stage process. First, a large subset of Wikipedia articles are scored - this score estimates the difficulty, or alternatively, the playability of the page. Then, the best articles are turned into cards by selecting, for each of them, a list of banned words based on its content. We believe that the game we present is more than mere entertainment and that, furthermore, this paper has pedagogical potential.

## introduction thanks to its considerable size -a total of more than 50 million articles in 300 different languages today -and its availability online, wikipedia has found many uses other than those of the traditional encyclopaedia. it is indeed frequently used for research in ai and natural language processing (nlp). for example, various large-scale machinereadable knowledge bases have been generated from the online encyclopedia, including yago (suchanek et al., 2007), yago2 (hoffart et al., 2013) or dbpedia (bizer et al., 2009), in addition to reading comprehension datasets such as squad (rajpurkar et al., 2016) and triviaqa (joshi et al., 2017). the plain text from wikipedia articles has also been used directly as the only source of knowl- @xcite the work presented in this paper is a personal project and is not directly related to my research at aist. edge for question-answering systems such as the one developed by chen et al. (2017). this article presents a system which uses wikipedia to generate the content of an application that is inspired by taboo, a word-guessing board game originally published by parker brothers in 1989. to play our version of the game, called "tabouid", all the group (of at least two) players require is a single electronic device (typically a smartphone). the game is divided in turns. during her turn (the length of which is defined by a countdown), the player looks at the card displayed on the screen. a card is composed of a title and a list of additional banned words (the words included in the title are considered banned words). see figures 1 and 2 for two screenshots of the application (the circle around the title of the card acts as a countdown). the player sets out to make the other players guess the title on the card (in its exact wording). to do so, she has to describe the concept to the other players but without using any of the banned words, nor any words constructed with the same stems (translations of the words into other languages are not allowed either). once a title has been guessed, the player continues on to the next card. the player has to skip the current card as soon as she mentions a banned word. the originality of tabouid lies in the fact that its content has been automatically generated from wikipedia using a range of nlp and machine learning techniques. this automated process means that tabouid can benefit from a wealth of 10,000 cards in english, and as many in french, covering not only words and linguistic expressions but also a variety of topics including artists, historical events or scientific concepts. in addition, all cards in tabouid are associated with a difficulty score. this allows the difficulty level of the game to be set in a straightforward way. with such an adaptable difficulty, the game can accommodate various groups of players, which could include individuals such as children or foreigners, whose level of proficiency or knowledge of the culture associated with the target language may vary. in addition, we believe that the work presented here can have some pedagogical value by defining an implementation project for students enrolled in nlp or computational linguistic programs. in this article, we describe how the cards were generated. each card corresponds to a wikipedia page. the title of the card is the title of the page, and the additional banned words (or expressions) are extracted from the text (and links) of the page. the automatic process is divided into two parts. first, we compute a difficulty score for each page of (a large subset of) wikipedia as described in section 2. then, for the best pages (i.e., the easiest ones), we select a list of banned words as described in section 3. you can currently play tabouid in english and french. the game is available as an android foot_0 and ios foot_1 application that is entirely free and does not contain any advertisement. the cards have been pre-generated and packaged with the application; an internet connection is not required during the game. ## page scoring the computation of difficulty scores is the most complex, and probably also the most crucial, part of the process. indeed, almost all of the pages in wikipedia would make for very poor cards, and so represent little to no interest to the game. they broadly fall into two categories. the first category is that of pages referring to entities lying very far from what we can consider common knowledge. for example (taken randomly from wikipedia), smrekovec lodge, described as "a mountain hostel on the southern slope of the smrekovec mountains" (in slovenia). the other category comprises pages that do refer to relatively familiar concepts, but that are way too specific and technical to be actually guessed in the context of the game. for example, taekwondo at the 2016 summer olympics -men's +80 kg. to do so, we use a neural network that takes as input a vector representation of a page and computes a real-valued score between 0 and 1. the lower the score, the more difficult the page. @xcite we first start by describing how the vector representation of each page is computed before turning to the neural network itself before describing the annotation collection. ## banned words selection a simplified version of our banned words selection algorithm is as follows. for a given wikipedia article, its text is first tokenised. tokens are then stemmed and all stop words are removed. finally, we select the 10 most frequent stems in the page and use their most frequent tokens (one for each) as banned words. tokenisation and stemming are performed with the nltk library (bird et al., 2009). there are two main differences between this simplification and the actual algorithm we use. the first is that we add rules to ntlk's stemmer in order to map strongly related words that are stemmed differently to the same class. for example, we send words stemmed as lawyer to the law class. we send the words stemmed as pole or poland (but starting with a capital letter) to the polish class. for all stems ending in pean, we remove the final ean (e.g., european is sent to europ). around 25 rules have been manually defined during the development of the english version of the game. second, we do not ban only single words, but also longer expressions. we detect links pointing to other wikipedia articles and consider their titles as potential banned expressions. also, because it appears that entities having a wikipedia page of their own are usually very informative even when they have comparatively few mentions in the text, we count them with a factor 1.5. given the name of a wikipedia article linked in the current page, each of its occurrences counts not only as 1 occurrence for each of the tokens it is composed of but also as 1.5 occurrences of the full name. for example, each occurrence of serge gainsbourg will count as 1 occurrence of serge, 1 occurrence of gainsbourg and 1.5 occurrences of serge gainsbourg. an occurrence of a single word entity name, such as madonna, simply counts as 1.5 occurrences of madonna. during the final step of the algorithm, when selecting the banned words or expressions, were two words composing the name of an entity to be selected, we only select this name instead. this algorithm is not perfect; some important words might be missed because of the way we analyse the page or simply because they do not even appear in the text. in addition, even with our hand-crafted rules supplementing the stemming algorithm, different forms of the same word (or words that, although different, are so strongly related that, according to the rules of the game, banning one is equivalent to banning the other) might be selected as distinct banned words. it is to compensate for such limitations that we build lists of 10 banned words or expressions. such a relatively high number tends to favour the introduction of false positives, but these are not a major problem. they are only an annoyance in that they unnecessarily slow the player, who has to read them. while we have not performed any quantitative evaluation of this algorithm, it has been, however, extensively tested during various parties, family gatherings, commuting trips and scientific conferences (among others). in addition to the two cards shown in figures 1 and 2 , here are a few other cards present in the game. world war i (score 1) with banned words france, russia, british army, forces, allied powers, battle, britain, ottoman, germany, and united states, rihanna (score 1) with banned words album, music, released, single, billboard, girl, song, record, awards, and featured, and artificial intelligence (score 0.98) with banned words ai, human, machine, research, learning, computer, problems, systems, networks, and algorithms. ## conclusion in this paper, we have shown how a range of relatively simple nlp and machine-learning techniques can be integrated effectively to automatically generate the content of tabouid, a wordguessing game freely available on android and ios devices. although easy to understand and implement, these techniques can be developed and improved on in many ways. they also naturally lead to a wide range of practical and theoretical questions relevant to nlp (e.g., data collection and annotation, and model interpretability). in this respect, this work could inspire implementation projects in nlp or computational linguistic programs. concerning the game itself, we believe that tabouid is more than just a fun game and can develop and help reinforce general knowledge for players of all backgrounds. it also appears to be an engaging way to practice speaking for language learners. in addition to improving the banned words selection process, future work on tabouid includes generating specific lists of cards based on school programs to use the game as an educational tool, using the category system of wikipedia to let users select more or less specific categories to play with, and adapting the algorithms to leverage the wide variety of languages wikipedia is available in beyond english and french. currently, the content of tabouid aims to reflect the diversity of wikipedia's encyclopaedic knowledge." as a consequence, some cards include words related to topics that might be deemed inappropriate for children. as suggested by an anonymous reviewer, another possible addition to the game could then be to predict the age appropriateness of a given topic, allowing for cards to be filtered on the basis of an age setting.