From d1f556260226f9f797ad15e3b6d40770b225e63b Mon Sep 17 00:00:00 2001 From: Jacob Torrey Date: Mon, 15 May 2023 11:47:31 -0600 Subject: [PATCH] Adding samples generated by chatGPT written in the voice of a highschooler Signed-off-by: Jacob Torrey --- ai-generated.txt | 56 ++++++++++++++++++++++++ samples/llm-generated/cgpt_hs_essay.txt | 15 +++++++ samples/llm-generated/cgpt_hs_essay2.txt | 17 +++++++ 3 files changed, 88 insertions(+) create mode 100644 samples/llm-generated/cgpt_hs_essay.txt create mode 100644 samples/llm-generated/cgpt_hs_essay2.txt diff --git a/ai-generated.txt b/ai-generated.txt index 738d943..748ad68 100644 --- a/ai-generated.txt +++ b/ai-generated.txt @@ -477,6 +477,42 @@ Remember to check local guidelines and opening hours for any attractions you pla Enjoy your stay in Sydney, and may your visit be filled with unforgettable experiences and cherished memories! +Title: The Urgent Need for Climate Action: A Call to Save Our Planet + +Introduction: + +Climate change is an issue that has gripped the world with its dire consequences. As an 11th grader concerned about our future, it is crucial to understand the impacts of climate change and the urgent need for immediate action. With rising temperatures, melting ice caps, and extreme weather events becoming the new norm, we must take responsibility for the well-being of our planet and future generations. + +Body: + +1. The Science Behind Climate Change: + +Let's start by understanding the science behind climate change. Our planet's atmosphere is composed of greenhouse gases (GHGs) like carbon dioxide, methane, and nitrous oxide. These gases trap heat from the sun, creating the natural greenhouse effect that keeps Earth warm enough for life. However, human activities, particularly the burning of fossil fuels and deforestation, have significantly increased GHG concentrations in the atmosphere, causing an enhanced greenhouse effect. This, in turn, leads to global warming and climate change. + +2. The Impacts of Climate Change: + +The impacts of climate change are far-reaching and affect various aspects of our lives. Rising temperatures result in the melting of polar ice caps, leading to a rise in sea levels. This not only endangers coastal communities but also threatens biodiversity and the delicate balance of ecosystems. Extreme weather events like hurricanes, droughts, and wildfires are becoming more frequent and intense, causing widespread destruction and loss of life. + +3. Environmental Justice and Climate Change: + +Climate change exacerbates existing social inequalities. Vulnerable communities, including low-income neighborhoods and marginalized groups, often bear the brunt of its consequences. Environmental justice is a crucial aspect of climate action. As an 11th grader, it is our responsibility to raise awareness about the disproportionate impacts on these communities and advocate for equitable solutions. + +4. The Role of Renewable Energy: + +Transitioning from fossil fuels to renewable energy sources is essential to combat climate change. Renewable energy, such as solar and wind power, produces fewer GHG emissions and offers a sustainable alternative to fossil fuels. By advocating for renewable energy policies and supporting the development of clean technologies, we can contribute to a greener and more sustainable future. + +5. Individual Actions Matter: + +While systemic change is crucial, individual actions also play a significant role. As 11th graders, we can make conscious choices in our daily lives to reduce our carbon footprint. Simple steps like conserving energy, reducing waste, and adopting sustainable transportation options can collectively make a substantial impact. + +6. The Importance of Education and Awareness: + +Education and awareness are key to fostering a collective understanding of climate change and its implications. As students, we can engage in environmental clubs, organize awareness campaigns, and advocate for climate education in schools. By empowering ourselves with knowledge, we become catalysts for change and inspire others to take action. + +Conclusion: + +Climate change poses an imminent threat to our planet, and as 11th graders, we cannot afford to turn a blind eye to this crisis. We have a responsibility to raise our voices, take action, and demand change from policymakers and leaders. By understanding the science behind climate change, promoting environmental justice, supporting renewable energy, and making sustainable choices in our daily lives, we can collectively work towards a greener, more sustainable future. The time for action is now, and it is up to us, the young generation, to lead the charge and save our planet for generations to come. + Abstract Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically de- tect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is re- ceived by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo. Introduction We present an approach for identifying the dialect of a speaker automatically solely based on text and on audio and text together. We compare the uni- modal approach to the bimodal one. There are no previous dialect identification approaches for Finnish. There are several situations were a dialect identification method can be of use. For example, if we have ASR models fine tuned for specific di- alects, the dialect identification from audio could be used as a preprocessing step. The model could also be used to label recorded materials automatically in order to create archival metadata. In order to make our contribution useful for others, we have released our code, models and processed data openly on GitHub1 and Zenodo2. Finnish is a large Uralic language that is one of the official languages of Finland, and is used essen- tially at all levels of the modern society. There are approximately five million Finnish speakers. The language belongs to the Finnic branch of the Uralic language family, and is very closely related to Kare- lian, Meänkieli and Kveeni, and is also closely re- lated to the Estonian language. It is more distantly related to numerous Uralic languages spoken in Russia. The history of written Finnish starts in the 16th century. Current orthography is connected to this written tradition, which developed into the current form in the late 19th century with a conscious plan- ning and systematic development of the lexicon. After this, the changes have been minor (Häkkinen, 1994, 16), and also impacted lexicon, especially what it comes to the development of the vocabu- lary of the modern society and traditional agrarian terminology becoming less known. The Finnish spoken language, however, is still largely based on Finnish dialects. In the 20th cen- tury some of the strongest dialectal features have been disappearing, but there are still clearly dis- tinguishable spoken vernacular varieties that are regionally marked. It has been shown that instead of clear disappearance of dialects there are vari- ous features that are spreading, but not at uniform rate, and reportedly younger speakers use the are- ally marked features less than the older speakers (Lappalainen, 2001, 92). Finnish vernaculars also represent historically rather different Finnic vari- eties, with major split between Eastern and Western dialects. There are, however, also dialect continu- ums and traditionally found gradual differentiation from region to region. Many of the changes have been lexical due to technical innovations and modernization of the society: orthographic spelling conventions have largely remained the same. Spoken Finnish, on the other hand, traditionally represents an areally di- vided dialect continuum, with several sharp bound- aries, and many regions of gradual differentiation from one municipality to another municipality. As mentioned, in the later parts of the 20th cen- tury relatively strong dialect leveling has been tak- ing place. Some of the Finnish dialects may already be concerned endangered, although the complex re- lationship between contemporary vernaculars and the most traditional dialectal forms makes this hard to ascertain. Dialect leveling in itself is a process known from many parts of Europe (Auer, 2018). However, in the case of Finnish the written stan- dard has remained relatively far from the spoken Finnish, besides individual narrow domains such as news broadcasts were the written form is used also in speech. Additionally there have been distinct text col- lections that include materials from this dialect archive. These include dialect books specific regions and municipalities, such as Oulun mur- rekirja [Dialect Book of Oulu] (Pääkkönen, 1994) or Savonlinnan seudun murrekirja [Dialect book of Savonlinna region] (Palander, 1986). There have also been more recent larger collections that contains excerpts from essentiallydialects (Lyytikäinen et al., 2013). Especially in the later parts of 21th century the spoken varieties have been leveling away from very specific local dialects, and although regional vari- eties still exist, most of the local varieties have certainly became endangered. Similar processes of dialect convergence have been reported from dif- ferent regions in Europe, although with substantial variation (Auer, 2018). In the case of Finnish this has not, however, resulted in merging of the written and spoken standards, but the spoken Finnish has remained, to our day, very distinct from the written standard. In a late 1950s, a program was set up to document extant spoken dialects, with the goal of recording 30 hours of speech from each municipal- ity. This work resulted in very large collections of dialectal recordings (Lyytikäinen, 1984, 448-449). Many of these have been published, and some por- tion has also been manually normalized. Dataset used is described in more detail in Section 3 Data. In Finnish linguistics the dialect identification has primarily been studied in the context of folk linguistics. In this line of research the perceptions of native speakers are investigated (Niedzielski and Preston, 2000). This type of studies have been done for Finnish, for example, by Mielikäinen and Palander (2014), Räsänen and Palander (2015) and Palander (2011). It has been possible to suggest for individual dialects which features are the most stable and will remain as local regional markers, and which seem to be in retention (Räsänen and Palander, 2015, 25). In this study we conduct just individual experiments and report their results, but in the further research we hope the results could be analyzed in more detail in connection with the earlier dialect perception studies, as we believe the differences in perceived dialect differences could be compared to the difficulties and successes the model has to differentiate individual varieties. Related work The current approaches to Finnish dialect have fo- cused on the textual modality only. Previously, bi- directional LSTM (long short-term memory) based models have been used to normalize Finnish di- alects to standard Finnish (Partanen et al., 2019) and to adapt standard Finnish text into different dialectal forms (Hämäläinen et al., 2020). Similar approach has also been used to normalize historical Finnish (Hämäläinen et al., 2021; Partanen et al., 2021). The closest research to our paper conducted for Finnish has been detection of foreign accents from audio. Behravan et al. (2013) have detected for- eign accents from audio only by using i-vectors. However, foreign accent detection is a very differ- ent task to native speaker dialect detection. Many foreign accents have clear cues through phonemes that are not part of the Finnish phonotactic system, where as with dialects, all phonemes are part of Finnish. There have been several recent approaches for Arabic to detect dialect from text (Balaji et al., 2020; Talafha et al., 2020; Alrifai et al., 2021). Textual dialect detection has been done also for German (Jauhiainen et al., 2018), Romanian (Za- haria et al., 2021) and Low Saxon (Siewert et al., 2020). The methods used range from traditional machine learning with features such as n-grams to neural models with pretrained embeddings, as it is typically the case in NLP research. None of these approaches use audio, as they rely on text only. At the same time, North Sami dialects have been identified from audio by training several models, kNNs, SVMs, RFs, CRFs, and LSTM, based on ex- tracted features (Kakouros et al., 2020). Kethireddy et al. (2020) use Mel-weighted SFF spectrogram to detect spoken Arabic dialects. Mel spectograms are also used by Draghici et al. (2020). All these approaches are mono-modal and use only audio. Based on our literature review, the existing ap- proaches use either text or audio for dialect detec- tion. We, however, use both modalities and apply them on a language with no existing dialect detec- tion models. Conclusions We have presented the first model for Finnish di- alect classification for a relatively large number of different dialects, 23 in total. Based on our ex- periments,text only model is not as effective in dialect classification as a model with text and audio. It is clear that the amount of data alone is not the only variable that constitutes a high performance of the model for a given dialect, but also how dis- tinctive a given dialect is from other dialects. Text and audio create interesting dialect vari- eties because they are the modalities for identifying dialects automatically solely based on text and on audio and text together. We present the first approach to automatically de- tect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is re- ceived by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo. Introduction We present an approach for identifying the dialect of a speaker automatically solely based on text and on audio and text together. We compare the uni- modal approach to the bimodal one. There are no previous dialect identification approaches for Finnish. Abstract Across many data domains, co-occurrence statis- tics about the joint appearance of objects are powerfully informative. By transforming unsu- pervised learning problems into decompositions of co-occurrence statistics, spectral algorithms provide transparent and efficient algorithms for posterior inference such as latent topic analysis and community detection. As object vocabularies grow, however, it becomes rapidly more expensive to store and run inference algorithms on co-occur- rence statistics. Rectifying co-occurrence, the key process to uphold model assumptions, becomes increasingly more vital in the presence of rare terms, but current techniques cannot scale to large vocabularies. We propose novel methods that si- multaneously compress and rectify co-occurrence statistics, scaling gracefully with the size of vo- cabulary and the dimension of latent space. We also present new algorithms learning latent vari- ables from the compressed statistics, and verify that our methods perform comparably to previous approaches on both textual and non-textual data. Introduction Understanding the underlying geometry of noisy and com- plex data is a fundamental problem of unsupervised learning. Probabilistic models explain data generation processes in terms of low-dimensional latent variables. Inferring a poste- rior distribution for these latent variables provides us with a compact representation for various exploratory analyses and downstream tasks (Bengio et al., 2013). However, exact inference is often intractable due to entangled interactions between the latent variables (Blei et al., 2003; Airoldi et al., 2008; A. Erosheva, 2003; Pritchard et al., 2000). Variational inference transforms the posterior approximation into an optimization problem over simpler distributions with in- dependent parameters (Jordan et al., 1999; Wainwright & Jordan, 2008; Blei et al., 2017), while Markov Chain Monte Carlo enables users to sample from the desired posterior distribution (Neal, 1993; Neal et al., 2011; Robert & Casella, 2013). However, these likelihood-based methods require numerous iterations without any guarantee beyond local improvement at each step (Kulesza et al., 2014). When the data consists of collections of discrete objects, co-occurrence statistics summarize interactions between objects. Collaborative filtering learns low-dimensional rep- resentations of individual items, which are useful for recom- mendation systems, by explicitly decomposing the co-oc- currence of items that are jointly consumed by certain users (Lee et al., 2015; Liang et al., 2016). Word-vector mod- els learn low-dimensional embeddings of individual words, which encode useful linguistic biases for neural networks, by implicitly decomposing the co-occurrence of words that appear together in contexts (Pennington et al., 2014; Levy & Goldberg, 2014). If co-occurrence provides a rich enough set of unbiased moments about an underlying generative model, spectral methods can provably learn posterior con- figurations from co-occurrence information alone, without iterating through individual training examples (Arora et al., 2013; Anandkumar et al., 2012c; Hsu et al., 2012; Anand- kumar et al., 2012b). However, two major limitations hinder users from taking advantage of spectral inference based on co-occurrence. First, the second-order co-occurrence matrix already grows quadratically in the number of words (e.g. objects, items, products). Pruning the vocabulary is an option, but for a retailer selling millions of long-tailed products, learning representations of only a subset of the products is inade- quate. Second, inference quality is poor in real data that does not necessarily follow our generative model. Whereas likelihood-based methods (Blei et al., 2003; Airoldi et al., 2008; A. Erosheva, 2003; Pritchard et al., 2000) have an intrinsic capability to fit the data to the model despite their mismatch, sample noise can easily destroy the performance of spectral methods even if the data is synthesized from the model (Kulesza et al., 2014; Lee et al., 2015). Rectification, a process of projecting empirical co-occur- rence onto a manifold consistent with the posterior geometry of the model, provides a principled treatment that improves the performance of spectral inference in the face of model- data mismatch (Lee et al., 2015). Alternating Projection rectification (AP) has been used to rectify the input co- occurrence matrix to the Anchor Word algorithm (AW), a second-order spectral topic model (Leeal., 2015; 2017; 2019), but running multiple projections dominates overall inference cost even when the vocabulary is small. AP makes the co-occurrence dense as well, exacerbating storage costs when operating on large vocabularies. In this paper, we propose two efficient methods that si- multaneously compress and rectify the co-occurrence ma- trix, scaling gracefully with the size of vo- cabulary and the dimension of latent space. We also propose a new approach that learns posterior con- figurations from the compressed statistics, enabling users to better interpret noisy and com- plex data even if the data is synthesized from the compressed statistics. Our experiments show that applying the p- ulary algorithm on a corpus of jade-white words grows the size of a football field even when the words are synthesized from the compressed statistics, rendering the data useless for neural networks. We also present new algorithms learning latent vari- ables from the compressed statistics, and verify that our methods perform comparably to previous approaches on both textual and non-textual data. Introduction Understanding the underlying geometry of noisy and com- plex data is a fundamental problem of unsupervised learning. Probabilistic models explain data generation processes in terms of low-dimensional latent variables. Inferring a poste- rior distribution for these latent variables provides us with a compact representation for various exploratory analyses and downstream tasks (Bengio et al., 2013). However, exact inference is often intractable due to entangled interactions between the latent variables (Blei et al., 2003; Airoldi et al., 2008; A. Erosheva, 2003; Pritchard et al., 2000). Variational inference transforms the posterior approximation into an optimization problem over simpler distributions with in- dependent parameters (Jordan et al., 1999; Wainwright & Jordan, 2008; Blei et al., 2017), while Markov Chain Monte Carlo enables users to sample from the desired posterior distribution (Neal, 1993; Neal et al., 2011; Robert & Casella, 2013). However, these likelihood-based methods require numerous iterations without any guarantee beyond local improvement at each step (Kulesza et al., 2014). When the data consists of collections of discrete objects, co-occurrence statistics summarize interactions between objects. Collaborative filtering learns low-dimensional rep- resentations of individual items, which are useful for recom- mendation systems, by explicitly decomposing the co-oc- currence of items that are jointly consumed by certain users (Lee et al., 2015; Liang et al., 2016). Word-vector mod- els learn low-dimensional embeddings of individual words, which encode useful linguistic biases for neural networks, by implicitly decomposing the co-occurrence of words that appear together in contexts (Pennington et al., 2014; Levy & Goldberg, 2014). If co-occurrence provides a rich enough set of unbiased moments about an underlying generative model, spectral methods can provably learn posterior con- figurations from co-occurrence information alone, without iterating through individual training examples (Arora et al., 2013; Anandkumar et al., 2012c; Hsu et al., 2012; Anand- kumar et al., 2012b). However, two major limitations hinder users from taking advantage of spectral inference based on co-occurrence. First, the second-order co-occurrence matrix already grows quadratically in the number of words (e.g. objects, items, products). Pruning the vocabulary is an option, but for a retailer selling millions of long-tailed products, learning representations of only a subset of the products is inade- quate. Second, inference quality is poor in real data that does not necessarily follow our generative model. Whereas likelihood-based methods (Blei et al., 2003; Airoldi et al., 2008; A. Erosheva, 2003; Pritchard et al., 2000) have an intrinsic capability to fit the data to the model despite their mismatch, sample noise can easily destroy the performance of spectral methods even if the data is synthesized from the model (Kulesza et al., 2014; Lee et al., 2015). Rectification, a process of projecting empirical co-occur- rence onto a manifold consistent with the posterior geometry of the model, provides a principled treatment that improves the performance of spectral inference in the face of model- data mismatch (Lee et al., 2015). @@ -509,4 +545,24 @@ Abstract Natural Language Processing (NLP) is a branch of artificial intelli- ge Abstract We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index. Keywords: Aggregation, Penalized Regression, Prediction, R, sentometrics, Textual Senti- ment, Time Series. Introduction Individuals, companies, and governments continuously consume written material from various sources to improve their decisions. The corpus of texts is typically of a high-dimensional longitudinal nature requiring statistical tools to extract the relevant information. A key source of information is the sentiment transmitted through texts, called textual sentiment. Algaba, Ardia, Bluteau, Borms, and Boudt (2020) review the notion of sentiment and its applications, mainly in economics and finance. They define sentiment as “the disposition of an entity towards an entity, expressed via a certain medium.” The medium in this case is texts. The sentiment expressed through texts may provide valuable insights on the future dynamics of variables related to firms, the economy, political agendas, product satisfaction, and marketing campaigns, for instance. Still, textual sentiment does not live by the premise to be equally useful across all applications. Deciphering when, to what degree, and which layers of the sentiment add value is needed to consistently study the full information potential present within qualitative communications. The econometric approach of constructing time series of sentiment by means of optimized selection and weighting of textual sentiment is referred to as sentometrics by Algaba et al. (2020) and Ardia, Bluteau, and Boudt (2019). The term sentometrics is a composition of (textual) sentiment analysis and (time series) econometrics. The release of the R (R Core Team 2021) text mining infrastructure tm (Feinerer, Hornik, and Meyer 2008) over a decade ago can be considered the starting point of the development and popularization of textual analysis tools in R. A number of successful follow-up attempts at improving the speed and interface of the comprehensive natural language processing capabil- ities provided by tm have been delivered by the packages openNLP (Hornik 2019), cleanNLP (Arnold 2017), quanteda (Benoit, Watanabe, Wang, Nulty, Obeng, Müller, and Matsuo 2018), tidytext (Silge and Robinson 2016), and qdap (Rinker 2020). The notable tailor-made packages for sentiment analysis in R are meanr (Schmidt 2019), SentimentAnalysis (Feuerriegel and Proellochs 2021), sentimentr (Rinker 2019b), and syuzhet (Jockers 2020). Many of these packages rely on one of the larger above-mentioned textual analysis infrastructures. The meanr package computes net sentiment scores fastest, but offers no flexibility.1 The SentimentAnalysis package relies on a similar calculation as used in tm’s sentiment scoring function. The package can additionally be used to generate and evaluate sentiment dictionaries. The sentimentr package extends the polarity scoring function from the qdap package to handle more difficult linguistic edge cases, but is therefore slower than packages which do not attempt this. The SentimentAnalysis and syuzhet packages also become comparatively slower for large input corpora. The quanteda and tidytext packages have no explicit sentiment computation function but their toolsets can be used to construct one. Our R package sentometrics proposes a well-defined modeling workflow, specifically targeted at studying the evolution of textual sentiment and its impact on other quantities. It can be used (i) to compute textual sentiment, (ii) to aggregate fine-grained textual sentiment into various sentiment time series, and (iii) to predict other variables with these sentiment measures. The combination of these three facilities leads to a flexible and computationally efficient framework to exploit the information value of sentiment in texts. The package presented in this paper therefore addresses the present lack of analytical capability to extract time series intelligence about the sentiment transmitted through a large panel of texts. Furthermore, the sentometrics package positions itself as both integrative and supplementary to the powerful textand data science toolboxes in the R universe. It is integrative, as it combines the strengths of quanteda and stringi (Gagolewski 2021) for corpus construction and manipulation. It uses data.table (Dowle and Srinivasan 2021) for fast aggregation of textual sentiment into time series, and glmnet (Friedman, Hastie, and Tibshirani 2010) and caret (Kuhn 2021) for (sparse) model estimation. The time series and time series packages also become available. The time series and time series packages also become available. The time series and time series packages also become available. The time series and time series packages also become available. The packages time series (Feinerer, Hornik, and Meyer 2008) and time series (Gagolewski and Srinivasan 2021). The release of the R (R Core Team 2021) text mining infrastructure tm (Feinerer, Hornik, and Meyer 2008) over a decade ago can be considered the starting point of the development and popularization of textual analysis tools in R. A number of successful follow-up attempts at improving the speed and interface of the comprehensive natural language processing capabil- ities provided by tm have been delivered by the packages openNLP (Hornik 2021), cleanNLP (Arnold 2017), quanteda (Benoit, Watanabe, Wang, Nulty, Obeng, Müller, and Matsuo 2018), tidytext (Silge and Robinson 2016), and qdap (Rinker 2020). +The Alchemist: A Journey of Self-Discovery + +Hey, fellow bookworms! Today, I want to dive into the incredible world of "The Alchemist" by Paulo Coelho. If you're into adventure, self-discovery, and a little bit of magic, this book is definitely for you. Strap in as we explore the life-changing journey of Santiago, an Andalusian shepherd boy, and the valuable life lessons he learns along the way. + +"The Alchemist" is set in the vibrant and mysterious land of Spain. Santiago, our young protagonist, starts off as an ordinary shepherd tending his sheep in the rolling hills of Andalusia. However, he's not content with his mundane life and dreams of finding hidden treasures and experiencing the wonders of the world. Who can't relate to that feeling of yearning for something more exciting? + +As the story unfolds, Santiago meets an intriguing character, the King of Salem, who encourages him to follow his Personal Legend, a destiny that leads each person to their true purpose in life. This concept really struck a chord with me, as I often wonder what my own Personal Legend might be. Santiago's journey becomes an allegorical representation of our own quests for self-discovery. + +Along his journey, Santiago encounters many fascinating individuals who teach him important life lessons. The crystal merchant, for example, teaches him about the danger of settling for mediocrity and the importance of pursuing one's dreams. This resonated with me because it's easy to fall into a routine and forget to pursue our passions. The crystal merchant's regret serves as a reminder to never let our dreams slip away. + +One of the most impactful characters in the book is the alchemist himself. This wise and enigmatic figure becomes Santiago's mentor, guiding him through the desert and imparting invaluable wisdom. The alchemist teaches Santiago the importance of listening to one's heart and connecting with the Soul of the World. It's a profound message that reminds us to trust our instincts and have faith in the universe. + +Coelho's writing style is simple yet powerful, making "The Alchemist" accessible and captivating for readers of all ages. His use of vivid imagery allows us to visualize the breathtaking landscapes Santiago encounters on his journey. From the vast Egyptian pyramids to the endless desert dunes, each description paints a picture in our minds that transports us right into the heart of the story. + +What I found particularly inspiring about "The Alchemist" is its exploration of the theme of perseverance. Santiago faces numerous setbacks and challenges, but he never gives up on his dreams. This resonated with me as a high school student, as I often feel overwhelmed by the pressures of academics and future uncertainties. Santiago's unwavering determination encouraged me to keep pushing forward, no matter how tough the journey may be. + +Another aspect of the book that I loved is the magical realism woven throughout the narrative. Coelho seamlessly blends elements of fantasy and spirituality with the realities of Santiago's journey. It's as if the magical moments serve as reminders that the universe is full of possibilities and that there's more to life than what meets the eye. This blend of realism and mysticism creates a sense of wonder and enchantment that keeps the reader engaged from start to finish. + +In conclusion, "The Alchemist" is a captivating tale of self-discovery and following one's dreams. Through Santiago's journey, we learn valuable life lessons about perseverance, listening to our hearts, and embracing the magic of the world around us. Coelho's simple yet evocative writing style and the book's themes of personal growth make it a must-read for anyone seeking inspiration and a deeper understanding of their own journey in life. So grab a copy, embark on this transformative adventure, and discover your own Personal Legend. Happy reading! + Abstract This work explores the capacities of character- based Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such ap- proaches to handle productive UGC phenom- ena, which almost by definition, cannot be seen at training time. Within a strict zero- shot scenario, we first study the detrimental impact on translation performance of various user-generated content phenomena on a small annotated dataset we developed, and then show that such models are indeed incapable of han- dling unknown letters, which leads to catastrophic translation failure once such characters are encountered. We further confirm this be- havior with a simple, yet insightful, copy task experiment and highlight the importance of reducing the vocabulary size hyper-parameter to increase the robustness of character-based models for machine translation. Introduction. Neural Machine Translation (NMT) models fall far short from being able to translate noisy User- Generated Content (UGC): the quality of their translation is often even worse than that of a traditional phrase-based system (Khayrallah and Koehn, 2018; Rosales Núñez et al., 2019). In addi- tion to ambiguous grammatical constructs and pro- fusion of ellipsis, the main difficulty encountered when translating UGC is the high number of out- of-vocabulary tokens (OOVs) resulting from mis- spelled words, emoticons, hashtags, mentions, and all specific constructs used in online forums and social medias (Foster, 2010; Seddah et al., 2012; Eisenstein, 2013; Sanguinetti et al., 2020). Some of those phenomena can be perceived as noise while the others are typical markers of language variation among speakers. Moreover, a certain amount of those same phenomena operate at the lexical level (either at the character, subword or word levels) (Sanguinetti et al., 2020). This is why, focusing more on the noise axis, char-based mod- els appear to offer a natural solution to this prob- lem (Luong and Manning, 2016; Ling et al., 2015): indeed these open vocabulary models are designed specifically to address the OOV problem. In this work, we explore the ability of out-of- the-box character-based NMT models (Lee et al., 2017) to address the challenges raised by UGC translation. While character-based models may seem promising for such task, to the best of our knowledge, they have only been tested either on data sets in which noise has been artificially added through sampling an edited word error data set (Be- linkov and Bisk, 2018; Ebrahimi et al., 2018a) and on canonical data set, in which they prove to be very effective for translating morphologically-rich languages with a high number of OOVs (Luong and Manning, 2016). However, our starting-points experiments show that character-based systems are outperformed by BPE models even when translating noisy UGC. To understand this counter-intuitive result, we con- duct several experiments and analyses. In partic- ular, we manually annotated 400 sentences at the token level using a fine-grained typology, to per- form our analyses. These sentences correspond to the worst and the best translated utterances of two MT systems (a char-based and a transformer-based model). Our results highlight the extreme sensi- bility of character-based models to the vocabulary size, a parameter often overlooked in the literature. Using a simple set of experiments, we thus show that these models are unable to perform an easy copy task due to their poor handling of unknown and rare characters. By adjusting the vocabulary size parameter, we drastically improve the robust- ness of our character-based model without causing a large drop in in-domain performance. Our contributions are as follows: • we provide an annotated data set that enables in-depth evaluations of the impact of UGC idiosyncrasies; • we demonstrate that char-based neural machine translation models are extremely sensi- tive to unknown and rare characters on both synthetic data and noisy user-generated content; • we show how an overlooked hyper-parameter drastically improve char-based MT models ro- bustness to natural noise while maintaining the in-domain level of performance. Conclusion We showed that in zero-shot scenarios, char-based models are not robust to UGC idiosyncrasies. We presented several experiments that explained this counter-intuitive result by an over-sensibility of these models to the vocabulary size. We demonstrated that drastically lowering this param- eterthe robustness of our char-based models when facing noisy UGC while maintaining almost the same level of performance for in-domain and out-of-domain canonical datasets. Interestingly, we noticed a lack in the literature regarding the importance of vocabulary considerations when training character-based NMTmodels. \ No newline at end of file diff --git a/samples/llm-generated/cgpt_hs_essay.txt b/samples/llm-generated/cgpt_hs_essay.txt new file mode 100644 index 0000000..96638cd --- /dev/null +++ b/samples/llm-generated/cgpt_hs_essay.txt @@ -0,0 +1,15 @@ +To Kill a Mockingbird: A Journey Through Prejudice and Justice + +To Kill a Mockingbird by Harper Lee is an absolutely incredible book that takes readers on a rollercoaster ride of emotions. Set in the 1930s in the small town of Maycomb, Alabama, it tells the story of Scout Finch, a young girl, and her brother Jem, as they navigate the complexities of racial prejudice and injustice. The book tackles important themes like racism, morality, and the loss of innocence, all while providing a thought-provoking and eye-opening experience. + +The first thing that struck me about To Kill a Mockingbird was its ability to capture the essence of Southern life during the Great Depression. Through Lee's vivid descriptions and engaging storytelling, I felt like I was right there in Maycomb, experiencing the scorching heat and tight-knit community firsthand. Lee has a knack for creating memorable characters, like the mysterious Boo Radley and the wise Atticus Finch, who resonate with readers long after they finish the book. + +The language and vocabulary used in To Kill a Mockingbird perfectly reflect the time period and the narrator's voice. Scout, who narrates the story, is a young and curious girl, and her language reflects her age and innocence. The author skillfully weaves in Southern dialect and idioms, adding authenticity to the narrative. For example, when Scout says, "I got somethin' to say. And then I ain't gonna say no more," it's as if we can hear her speaking right in front of us. The colloquial language not only adds to the story's charm but also helps us connect with the characters on a deeper level. + +One of the most powerful aspects of To Kill a Mockingbird is its exploration of racism and prejudice. The book exposes the deep-rooted racial prejudices that existed in the American South during that time, and it does so through the lens of a child's innocence. As Scout and Jem witness the trial of Tom Robinson, a black man accused of assaulting a white woman, they come face-to-face with the harsh realities of discrimination. Lee masterfully portrays the injustice and hypocrisy of a society that is quick to judge based on the color of one's skin rather than the content of their character. + +Atticus Finch, Scout and Jem's father, stands as a shining example of moral integrity and justice in the face of adversity. He embodies the qualities of a true hero, defending Tom Robinson despite knowing the town's prejudice would be working against him. Atticus's unwavering belief in equality and his willingness to stand up for what is right serve as a powerful inspiration. He teaches Scout and Jem valuable life lessons about empathy, compassion, and the importance of fighting for justice, no matter the odds. + +The loss of innocence is another theme that runs throughout the book. As Scout and Jem grow older and witness the harsh realities of the world, their innocence begins to crumble. They learn that not everyone possesses the same sense of morality and fairness as their father. This loss of innocence is most evident in the realization that their town, which they once believed to be idyllic and just, is plagued by racism and prejudice. It is a heartbreaking and sobering moment that resonates with readers, forcing us to confront the harsh realities of our own society. + +To Kill a Mockingbird is not just a story about racial injustice; it is a timeless tale that addresses universal themes. It serves as a poignant reminder that we must confront and challenge the prejudices that exist in our own communities. By encouraging readers to question societal norms and preconceived notions, Lee invites us to be active participants in the fight for equality and justice. \ No newline at end of file diff --git a/samples/llm-generated/cgpt_hs_essay2.txt b/samples/llm-generated/cgpt_hs_essay2.txt new file mode 100644 index 0000000..293c694 --- /dev/null +++ b/samples/llm-generated/cgpt_hs_essay2.txt @@ -0,0 +1,17 @@ +To Kill a Mockingbird by Harper Lee is a classic novel that has been assigned to high school English classes for decades. At first, I was skeptical of the book because it's an older work of fiction and I was afraid it would be boring. However, once I started reading, I was immediately drawn into the story of Scout Finch and her experiences growing up in a small town in Alabama during the 1930s. + +The book is written in a very conversational tone, and it's easy to get lost in the story. Scout is a great narrator because she's so honest and straightforward. She tells it like it is, without any pretense or artifice. The language used in the book is simple and straightforward, which makes it accessible to readers of all ages and backgrounds. + +One of the most compelling aspects of the book is the way it deals with issues of race and class. The story takes place during a time when segregation was still legal in the United States, and black people were treated as second-class citizens. This is something that is hard for me to imagine, growing up in a more enlightened era. But the book does a great job of showing just how deeply ingrained racism was in society at the time. + +Atticus Finch, Scout's father, is the moral center of the book. He's a lawyer who is assigned to defend a black man named Tom Robinson who has been accused of raping a white woman. Atticus knows that the odds are against him, but he takes on the case anyway because he believes it's the right thing to do. His commitment to justice and equality is inspiring, and it's hard not to feel a sense of admiration for him. + +One of the things I appreciated about the book is the way it shows that racism isn't just an issue of individual prejudice. It's something that is deeply embedded in the social structures of society. For example, Tom Robinson is found guilty despite the fact that there is clear evidence that he is innocent. This is because the jury is made up of white people who are biased against black people. It's a powerful reminder that even the most well-intentioned people can be influenced by their environment. + +Another important theme of the book is the importance of empathy and compassion. Scout learns this lesson through her interactions with Boo Radley, a reclusive neighbor who is feared and misunderstood by many in the town. Through a series of events, Scout comes to realize that Boo is not the monster she imagined him to be. She sees him as a vulnerable human being who has been damaged by his experiences. This is a powerful message about the importance of looking beyond appearances and treating others with kindness and understanding. + +To Kill a Mockingbird is a beautifully written book that deals with complex themes in a way that is accessible to readers of all ages. It's a powerful reminder of the importance of justice, equality, and empathy. The book is a timeless classic that still resonates with readers today. + +One of the things I found interesting about the book is the way it deals with the issue of gender. Scout is a tomboy who doesn't fit into the traditional mold of what it means to be a girl. She's not interested in dresses or makeup, and she's more comfortable playing with boys than with girls her own age. This is something that makes her stand out in her small town, and it's something that I can relate to as well. + +Scout's unconventional personality is one of the things that makes her such a great narrator. She's not afraid to speak her mind, even if it means going against the norms of her society. She's a rebel in her own way, and this makes her a compelling character to follow throughout the book. \ No newline at end of file