Kolloquium FS 2021: Berichte aus der aktuellen Forschung am Institut, Bachelor- und Master-Arbeiten, Programmierprojekte, Gastvorträge
Speaker & Topic
Overview of New Projects
Sarah Ebling: EU project EASIER, SNF project SMILE II
Anastassia Shaitarova & Anne Göhring: The Impact of Computer-generated Language (NCCR Evolving Language)
Gerold Schneider: Hate-speech Detection (UFSP Digital Religion)
Martin Volk: Bullinger digital
|Thursday, 18. March, 17.15h||Sebastian Ruder: Cross-lingual Transfer Learning|
Hanna Fechner: Search in Semantic Memory across the Lifespan
Nora Hollenstein: Leveraging Cognitive Processing Signals for Natural Language Understanding
Susie Rao: Fraud Detection in E-commerce
Duygu Ataman: Character-level Neural Machine Translation: History and Challenges
Phillip Ströbel: Learning to read the Bullinger Correspondence - Handwritten Text Recognition for 16th Century Letters
Chantal Amrhein: Subwords or Characters? - Evaluating Segmentation Strategies on Morphological Phenomena in Machine Translation
Jannis Vamvas: Does Distillation Amplify Bias in Machine Translation? Assessing Disambiguation with Source Scoring
Ann-Sophie Gnehm: Text Mining on Job Advertisements
Norbert Fuchs: The Law of Inertia and the Frame Problem in Attempto Controlled English
Anne Göhring + Manfred Klenner: Sentiment Inference: The Identification of Negative Actors
Susi Rao, "Fraud Detection in E-commerce", 13. April 2021
A Graph Neural Network (GNN) is a type of neural network which directly operates on graph-structured data with message passing and aggregation. Popular variants include graph convolutional networks (GCN), graph attention networks (GAT), and GraphSAGE. The use of GNNs are found in many state-of-the-art applications like recommender systems, knowledge graphs, and fraud detection. In the NLP community, many researchers have been adopting GNNs in tasks where organizing data in a graph-structured fashion is natural, e.g., text classification, sequence labeling, neural machine translation, relation extraction, event extraction, and text generation.
In this talk, I present our work with Ebay on fraud detection using GNNs. At online retail platforms, it is crucial to actively detect risks of fraudulent transactions to improve the customer experience, minimize loss, and prevent unauthorized chargebacks. Traditional rule-based methods and simple feature-based models are either inefficient or brittle and uninterpretable.
The graph structure that exists among the heterogeneous typed entities of the transaction logs is informative and difficult to fake. To utilize the heterogeneous graph relationships and enrich the explainability, we present xFraud, an explainable fraud transaction prediction system. In our experiments on two real transaction networks with up to ten millions transactions, we achieved an area under a curve (AUC) score that outperforms baseline models and graph embedding methods. In addition, we show how the explainer benefits model predictions and enhances model trustworthiness for real-world fraud transaction cases.
Duygu Ataman, "Character-level Neural Machine Translation: History and Challenges", 13. April 2021
Neural machine translation (NMT) models typically operate over a fixed-size vocabulary which can be problematic in morphologically-rich languages with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic. This problem is typically addressed by either pre-processing words into subword units or performing translation directly at the level of characters. The former is based on word segmentation algorithms optimized using corpus-level statistics with no regard to the translation task. The latter approach has shown significant benefits for translating morphologically-rich languages, although practical applications are still limited due to increased requirements in terms of model capacity. In this talk, we present an overview of recent character-based approaches to NMT and open challenges related to their future deployment.
Dr. Hanna B. Fechner, «Search in Semantic Memory across the Lifespan», 23.03.2021
Search in memory is essential for mastering tasks of everyday life. Search success depends on two aspects of cognition: People’s capacities for retrieving and maintaining information and strategies for navigating the search using retrieval cues. During normal aging, cognitive capacities change substantially. Age-related differences in memory search were found in semantic-fluency tasks in which participants search as much information as possible from a specific domain in a limited amount of time. In these studies, older (compared with younger) adults recalled less information, switched between semantic categories less frequently, and switched more frequently between information that often occurs together. Previous research concluded that mechanisms of decline in capacities impair memory search in older adults.
This project contrasts mechanisms of declining capacities with mechanisms of compensatory strategic adjustments to age-related changes in retrieval efficiency. These strategic mechanisms rely on retrieval quality of recalled information to determine the best cues for guiding the search. We test the candidate mechanisms implemented as computational cognitive models in a Bayesian framework on cross-sectional and longitudinal data from semantic-fluency tasks of the Berlin Aging Study. The models share computational assumptions from the cognitive architecture ACT-R and receive activation levels for semantic memories that are estimated from large text corpora. Our model comparison will show which candidate mechanisms best explain the data. This will reveal to what extent strategic adjustments beyond decline contribute to our understanding of older adults’ memory search.
Dr. Nora Hollenstein, "A human-centered approach to Natural Language Processing", 23.03.2021
As we know, natural language processing (NLP) has made great progress in recent years. The progress of NLP and machine learning (ML) is intricately linked with human behavior. In this talk, I will present two projects on opposite ends of the NLP pipeline related to human-centred ML. First, we analyze human labelling behavior. What happens in the brain while we annotate? What patterns of human behavior emerge in crowd-sourcing settings? On the other end of the pipeline, we use human language processing signals to evaluate NLP models. How well do word embeddings align with brain representations? Do state-of-the-art transformer language models reflect human reading behavior? Within these projects, we approach two key challenges in machine learning: gathering labelled data and explaining the inner workings of NLP models.