4 citations · 8 across the 34 of their papers we have counts for
14 papers · 1 filter
Utilizing Multimodal Data for Edge Case Robust Call-sign Recognition and Understanding
Alexander Blatt, Dietrich Klakow
Operational machine-learning based assistant systems must be robust in a wide range of scenarios. This hold especially true for the air-traffic control (ATC) domain. The robustness…
WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case
Vagrant Gautam, Julius Steuer, Eileen Bingert +3
While measuring bias and robustness in coreference resolution are important goals, such measurements are only as good as the tools we use to measure them. Winogender Schemas (Rudin…
Improving Semantic Understanding in Speech Language Models via Brain-tuning
Omer Moussa, Dietrich Klakow, Mariya Toneva
Speech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating t…
Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?
Anupama Chingacham, Miaoran Zhang, Vera Demberg +1
Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text. However, instructing LLMs to generate text that…
Block-Operations: Using Modular Routing to Improve Compositional Generalization
Florian Dietz, Dietrich Klakow
We explore the hypothesis that poor compositional generalization in neural networks is caused by difficulties with learning effective routing. To solve this problem, we propose the…
Comgra: A Tool for Analyzing and Debugging Neural Networks
Florian Dietz, Sophie Fellenz, Dietrich Klakow +1
Neural Networks are notoriously difficult to inspect. We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations o…
Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
Pin-Jie Lin, Miaoran Zhang, Marius Mosbach +1
Identifying beneficial tasks to transfer from is a critical step toward successful intermediate-task transfer learning. In this work, we experiment with 130 source-target task comb…
Learning Program Behavioral Models from Synthesized Input-Output Pairs
Tural Mammadov, Dietrich Klakow, Alexander Koller +1
We introduce Modelizer - a novel framework that, given a black-box program, learns a model from its input/output behavior using neural machine translation algorithms. The resulting…
Joint vs Sequential Speaker-Role Detection and Automatic Speech Recognition for Air-traffic Control
Alexander Blatt, Aravind Krishnan, Dietrich Klakow
Utilizing air-traffic control (ATC) data for downstream natural-language processing tasks requires preprocessing steps. Key steps are the transcription of the data via automatic sp…
On the Encoding of Gender in Transformer-based ASR Representations
Aravind Krishnan, Badr M. Abdullah, Dietrich Klakow
While existing literature relies on performance differences to uncover gender biases in ASR models, a deeper analysis is essential to understand how gender is encoded and utilized…
From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP
Marius Mosbach, Vagrant Gautam, Tomás Vergara-Browne +2
Interpretability and analysis (IA) research is a growing subfield within NLP with the goal of developing a deeper understanding of the behavior or inner workings of NLP systems and…
Understanding "Democratization" in NLP and ML Research
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow +1
Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the "democratizat…
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?
Dawei Zhu, Pinzhen Chen, Miaoran Zhang +3
Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.…
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Dawei Zhu, Sony Trenous, Xiaoyu Shen +3
Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of paralle…