70 research outputs found
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules
We introduce a novel data generation method for contradiction detection,
which leverages the generative power of large language models as well as
linguistic rules. Our vision is to provide a condensed corpus of prototypical
contradictions, allowing for in-depth linguistic analysis as well as efficient
language model fine-tuning. To this end, we instruct the generative models to
create contradicting statements with respect to descriptions of specific
contradiction types. In addition, the model is also instructed to come up with
completely new contradiction typologies. As an auxiliary approach, we use
linguistic rules to construct simple contradictions such as those arising from
negation, antonymy and numeric mismatch. We find that our methods yield
promising results in terms of coherence and variety of the data. Further
studies, as well as manual refinement are necessary to make use of this data in
a machine learning setup
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Exploration and Skill Acquisition in a Major Online Game
Using data from a major commercial online game, Destiny, we track the development of player skill across time. From over 20,000 player record we identify 3475 players who have played on 50 or more days. Our focus is on how variability in elements of play affect subsequent skill development. After validating the persistent influence of differences in initial performance between players, we test how practice spacing, social play, play mode variability and a direct measure of game-world exploration affect learning rate. These latter two factors do not affect learning rate. Players who space their practice more learn faster, in line with our expectations, whereas players who coordinate more with other players learn slower, which contradicts our initial hypothesis. We conclude that not all forms of practice variety expedite skill acquisition. Online game telemetry is a rich domain for exploring theories of optimal skill acquisition
Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE
In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms\u27 survival. Predictions of customers\u27 Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks
Informed Named Entity Recognition Decoding for Generative Language Models
Ever-larger language models with ever-increasing capabilities are by now
well-established text processing tools. Alas, information extraction tasks such
as named entity recognition are still largely unaffected by this progress as
they are primarily based on the previous generation of encoder-only transformer
models. Here, we propose a simple yet effective approach, Informed Named Entity
Recognition Decoding (iNERD), which treats named entity recognition as a
generative process. It leverages the language understanding capabilities of
recent generative models in a future-proof manner and employs an informed
decoding scheme incorporating the restricted nature of information extraction
into open-ended text generation, improving performance and eliminating any risk
of hallucinations. We coarse-tune our model on a merged named entity corpus to
strengthen its performance, evaluate five generative language models on eight
named entity recognition datasets, and achieve remarkable results, especially
in an environment with an unknown entity class set, demonstrating the
adaptability of the approach.Comment: 12 pages, 2 figures, 4 table
Solving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis
Many applications in automated auditing and the analysis and consistency
check of financial documents can be formulated in part as the subset sum
problem: Given a set of numbers and a target sum, find the subset of numbers
that sums up to the target. The problem is NP-hard and classical solving
algorithms are therefore not practical to use in many real applications. We
tackle the problem as a QUBO (quadratic unconstrained binary optimization)
problem and show how gradient descent on Hopfield Networks reliably finds
solutions for both artificial and real data. We outline how this algorithm can
be applied by adiabatic quantum computers (quantum annealers) and specialized
hardware (field programmable gate arrays) for digital annealing and run
experiments on quantum annealing hardware.Comment: To be published in proceedings of IEEE International Conference on
Machine Learning Applications IEEE ICMLA 202
KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
We present KPI-BERT, a system which employs novel methods of named entity
recognition (NER) and relation extraction (RE) to extract and link key
performance indicators (KPIs), e.g. "revenue" or "interest expenses", of
companies from real-world German financial documents. Specifically, we
introduce an end-to-end trainable architecture that is based on Bidirectional
Encoder Representations from Transformers (BERT) combining a recurrent neural
network (RNN) with conditional label masking to sequentially tag entities
before it classifies their relations. Our model also introduces a learnable
RNN-based pooling mechanism and incorporates domain expert knowledge by
explicitly filtering impossible relations. We achieve a substantially higher
prediction performance on a new practical dataset of German financial reports,
outperforming several strong baselines including a competing state-of-the-art
span-based entity tagging approach.Comment: Accepted at ICPR 2022, 8 pages, 1 figure, 6 table
Predicting Victory in a Hybrid Online Competitive Game : The Case of Destiny
Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multiplayer competitive games with mixed genres is addressed. The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99% in terms of average precision, with a higher performance recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games
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